Human Dorsolateral Prefrontal Cortex Research Articles

MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning

Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integratingthegene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methodsin cell classification. In this work, we propose MVCLST, a multi-view comparative learningmethod to analyze spatial transcriptomicsdata for accurate cell type classification. MVCLSTconstructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to clusterthe learned featuresfor cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomicsdata analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. Italso outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulbdata.

Volume electron microscopy reveals 3D synaptic nanoarchitecture in postmortem human prefrontal cortex.

Synaptic function is directly reflected in quantifiable ultrastructural features using electron microscopy (EM) approaches. This coupling of synaptic function and ultrastructure suggests that in vivo synaptic function can be inferred from EM analysis of ex vivo human brain tissue. To investigate this, we employed focused ion beam-scanning electron microscopy (FIB-SEM), a volume EM (VEM) approach, to generate ultrafine-resolution, three-dimensional (3D) micrographic datasets of postmortem human dorsolateral prefrontal cortex (DLPFC), a region with cytoarchitectonic characteristics distinct to human brain. Synaptic, sub-synaptic, and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, 3D neuropil reconstruction revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of heightened synaptic communication, integration, and plasticity. Altogether, our findings provide critical proof-of-concept data demonstrating that ex vivo VEM analysis is an effective approach to infer in vivo synaptic functioning in human brain.

Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. The spatial architecture of cell types and gene expression are the basis of cell-cell interactions, biological function and disease pathology, but are not well investigated in the human motor cortex, a key ALS-relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, the brain regional vulnerability of ALS-associated genes and the genetic link between region-specific genes and ALS risk remain largely unclear. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics. We benchmarked SpatialE against another enrichment tool (multimodal intersection analysis) using spatial transcriptomics data from both human and mouse brain tissues. To investigate regional vulnerability, we analysed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function variants detected by whole-genome sequencing in ALS patients and controls, then analysed differential gene expression in the TargetALS RNA-sequencing dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than multimodal intersection analysis in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogeneous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 of the motor cortex, with abundant expression of upper motor neurons and layer 5 excitatory neurons. Burden analyses of rare loss-of-function variants in Layer 5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6814 ALS patients and 3324 controls (P = 0.029). Gene expression analyses in CNS tissues revealed downregulation of NOMO1 in ALS, which is consistent with a loss-of-function disease mechanism. In conclusion, our integrated spatial transcriptomics and genomic analyses identified regional brain vulnerability in ALS and the association of a layer 5 gene (NOMO1) with ALS risk.

Deciphering prefrontal circuits underlying stress and depression: exploring the potential of volume electron microscopy.

Adapting to environmental changes and formulating behavioral strategies are central to the nervous system, with the prefrontal cortex being crucial. Chronic stress impacts this region, leading to disorders including major depression. This review discusses the roles for prefrontal cortex and the effects of stress, highlighting similarities and differences between human/primates and rodent brains. Notably, the rodent medial prefrontal cortex is analogous to the human subgenual anterior cingulate cortex in terms of emotional regulation, sharing similarities in cytoarchitecture and circuitry, while also performing cognitive functions similar to the human dorsolateral prefrontal cortex. It has been shown that chronic stress induces atrophic changes in the rodent mPFC, which mirrors the atrophy observed in the subgenual anterior cingulate cortex and dorsolateral prefrontal cortex of depression patients. However, the precise alterations in neural circuitry due to chronic stress are yet to be fully unraveled. The use of advanced imaging techniques, particularly volume electron microscopy, is emphasized as critical for the detailed examination of synaptic changes, providing a deeper understanding of stress and depression at the molecular, cellular and circuit levels. This approach offers invaluable insights into the alterations in neuronal circuits within the medial prefrontal cortex caused by chronic stress, significantly enriching our understanding of stress and depression pathologies.

Proteomic networks of gray and white matter reveal tissue-specific changes in human tauopathy.

To define tauopathy-associated changes in the human gray and white matter proteome. We applied tandem mass tagged labeling and mass spectrometry, consensus, and ratio weighted gene correlation network analysis (WGCNA) to gray and white matter sampled from postmortem human dorsolateral prefrontal cortex. The sampled tissues included control as well as Alzheimer's disease, corticobasal degeneration, progressive supranuclear palsy, frontotemporal degeneration with tau pathology, and chronic traumatic encephalopathy. Only eight proteins were unique to gray matter while six were unique to white matter. Comparison of the gray and white matter proteome revealed an enrichment of microglial proteins in the white matter. Consensus WGCNA sorted over 6700 protein isoforms into 46 consensus modules across the gray and white matter proteomic networks. Consensus network modules demonstrated unique and shared disease-associated microglial and endothelial protein changes. Ratio WGCNA sorted over 6500 protein ratios (white:gray) into 33 modules. Modules associated with mitochondrial proteins and processes demonstrated higher white:gray ratios in diseased tissues relative to control, driven by mitochondrial protein downregulation in gray and upregulation in white. The dataset is a valuable resource for understanding proteomic changes in human tauopathy gray and white matter. The identification of unique and shared disease-associated changes across gray and white matter emphasizes the utility of examining both tissue types. Future studies of microglial, endothelial, and mitochondrial changes in white matter may provide novel insights into tauopathy-associated changes in human brain.

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data

BackgroundAdvances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis.ResultsExtensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity.ConclusionsHyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex.

We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability. This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP). In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach. Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP. Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Uncover spatially informed variations for single-cell spatial transcriptomics with STew.

The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues. Source code and the R software tool STew are available from github.com/fanzhanglab/STew.

Mapping cortical excitability in the human dorsolateral prefrontal cortex

BackgroundTranscranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (dlPFC) is an effective treatment for depression, but the neural effects after TMS remains unclear. TMS paired with electroencephalography (TMS-EEG) can causally probe these neural effects. Nonetheless, variability in single pulse TMS-evoked potentials (TEPs) across dlPFC subregions, and potential artifact induced by muscle activation, necessitate detailed mapping for accurate treatment monitoring. ObjectiveTo characterize early TEPs anatomically and temporally (20–50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and specifically that conditions with larger muscle artifact may exhibit lower observed EL-TEPs due to over-rejection during preprocessing. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. MethodsIn 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. ResultsStimulation location significantly influenced observed EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. SignificanceEL-TEPs can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols.

A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex.

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

Elucidating the underlying components of metacognitive systematic bias in the human dorsolateral prefrontal cortex and inferior parietal cortex

Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.

Cellular senescence induced by cholesterol accumulation is mediated by lysosomal ABCA1 in APOE4 and AD.

Cellular senescence is a hallmark of aging and has been implicated in Alzheimer's disease (AD) pathogenesis. Cholesterol accumulation drives cellular senescence; however, the underlying mechanisms are unclear. ATP-binding cassette transporter A1 (ABCA1) plays an important role in cholesterol homeostasis. ABCA1 expression and its trafficking is afiltered in APOE4 and AD cellular and mouse models. However, whether ABCA1 trafficking is involved in cellular senescence in APOE4 and AD remains unknown. We examined the association between cellular senescence and ABCA1 expression in human postmortem brain samples using transcriptomic, histological, and biochemical analyses. An unbiased proteomic screening was performed to identify targets that mediate cellular ABCA1 trafficking. APOE4-TR mice, immortalized, primary and induced pluripotent stem cell (iPSC) models were used to examine the cholesterol-ABCA1-senescence pathways. Bulk and single nuclei transcriptomic profiling of the human dorsolateral prefrontal cortex from the Religious Order Study/Memory Aging Project (ROSMAP) revealed upregulation of cellular senescence transcriptome signatures in AD, which was strongly correlated with ABCA1 expression. Immunofluorescence and immunoblotting analyses confirmed increased ABCA1 expression in AD brain tissues, which was associated with lipofuscin-stained lipids and mTOR phosphorylation. Using discovery proteomics, caveolin-1, a sensor of cellular cholesterol accumulation, was identified to promote ABCA1 endolysosomal trafficking. Greater caveolin-1 expression was found in both APOE4-TR mouse models and AD human brains. Cholesterol induced mTORC1 activation was regulated by ABCA1 expression or its lysosomal trapping. Reducing cholesterol by cyclodextrin in APOE4-TR mice reduced ABCA1 lysosome trapping and increased ABCA1 recycling to efflux cholesterol to HDL particles, reducing mTORC1 activation and senescence-associated neuroinflammation. In human iPSC-derived astrocytes, the reduction of cholesterol by cyclodextrin attenuated inflammatory responses. Cholesterol accumulation in APOE4 and AD induced caveolin-1 expression, which traps ABCA1 in lysosomes to activate mTORC1 pathways and induce cellular senescence. This study provided novel insights into how cholesterol accumulation in APOE4 and AD accelerates senescence.

Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex.

We currently lack a reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC). We recently found that the strength of early and local dlPFC transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely across dlPFC subregions. Despite these differences in response amplitude, reliability at each target is unknown. Here we quantified within-session reliability of dlPFC EL-TEPs after TMS to six left dlPFC subregions in 15 healthy subjects. We evaluated reliability (concordance correlation coefficient [CCC]) across targets, time windows, quantification methods, regions of interest, sensor- vs. source-space, and number of trials. On average, the medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). However, all targets except the most anterior were reliable (CCC > 0.7) using at least one combination of the analytical parameters tested. Longer (20 to 60ms) and later (30 to 60ms) windows increased reliability compared to earlier and shorter windows. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials at a medial dlPFC target. Overall, medial dlPFC targeting, wider windows, and peak-to-peak quantification improved reliability. With careful selection of target and analytic parameters, highly reliable EL-TEPs can be extracted from the dlPFC after only a small number of trials.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

New organizational principles and 3D cytoarchitectonic maps of the dorsolateral prefrontal cortex in the human brain.

Areas of the dorsolateral prefrontal cortex (DLPFC) are part of the frontoparietal control, default mode, salience, and ventral attention networks. The DLPFC is involved in executive functions, like working memory, value encoding, attention, decision-making, and behavioral control. This functional heterogeneity is not reflected in existing neuroanatomical maps. For example, previous cytoarchitectonic studies have divided the DLPFC into two or four areas. Macroanatomical parcellations of this region rely on gyri and sulci, which are not congruent with cytoarchitectonic parcellations. Therefore, this study aimed to provide a microstructural analysis of the human DLPFC and 3D maps of cytoarchitectonic areas to help address the observed functional variability in studies of the DLPFC. We analyzed ten human post-mortem brains in serial cell-body stained brain sections and mapped areal boundaries using a statistical image analysis approach. Five new areas (i.e., SFG2, SFG3, SFG4, MFG4, and MFG5) were identified on the superior and middle frontal gyrus, i.e., regions corresponding to parts of Brodmann areas 9 and 46. Gray level index profiles were used to determine interregional cytoarchitectural differences. The five new areas were reconstructed in 3D, and probability maps were generated in commonly used reference spaces, considering the variability of areas in stereotaxic space. Hierarchical cluster analysis revealed a high degree of similarity within the identified DLPFC areas while neighboring areas (frontal pole, Broca's region, area 8, and motoric areas) were separable. Comparisons with functional imaging studies revealed specific functional profiles of the DLPFC areas. Our results indicate that the new areas do not follow a simple organizational gradient assumption in the DLPFC. Instead, they are more similar to those of the ventrolateral prefrontal cortex (Broca's areas 44, 45) and frontopolar areas (Fp1, Fp2) than to the more posterior areas. Within the DLPFC, the cytoarchitectonic similarities between areas do not seem to follow a simple anterior-to-posterior gradient either, but cluster along other principles. The new maps are part of the publicly available Julich Brain Atlas and provide a microstructural reference for existing and future imaging studies. Thus, our study represents a further step toward deciphering the structural-functional organization of the human prefrontal cortex.

Increased pituitary adenylate cyclase-activating peptide genes expression in the prefrontal cortex in schizophrenia in relation to suicide.

Pituitary adenylate cyclase-activating peptide (PACAP) is a stress-related neuropeptide that is produced in several brain areas. It acts by 3 receptors: PACAP type-1 (PAC1), vasoactive intestinal peptide (VIP) -1 and -2 (VPAC1 and 2). Data on polymorphisms in PACAP and PAC1 indicate a relationship of the PACAP system with schizophrenia (SCZ). The prefrontal cortex was chosen to measure PACAP-gene related expression changes, since this is a central structure in the symptoms of schizophrenia (SCZ). We investigated alterations in the expression of the PACAP-related genes by qPCR in the human dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) of 35 SCZ patients and 34 matched controls in relation to SCZ, suicide, gender and medication. The ACC revealed an upregulation in PACAP, PAC1, VPAC1 and VPAC2 in SCZ suicide (S) completers compared to controls. An increase in PACAP, VPAC1 and VPAC2 expression was also present in the ACC in SCZ-S compared to SCZ patients who died naturally (SCZ-N). In the DLPFC, an increase in PAC1 was found in SCZ-N patients compared to SCZ-S and controls. Moreover, an increase in all PACAP-related genes was present in SCZ-N male patients compared to SCZ-N females. Concluding, expression changes were found in PACAP-related genes in relation to SCZ, suicide and gender. In particular, there was a higher PACAP-related gene expression in SCZ patients in the ACC in relation to suicide and in DLPFC in relation to SCZ. These findings suggest a potential link between PACAP and the pathophysiology of SCZ and suicide. Further research is needed to understand the functional significance and potential clinical applications of these changes.

ScribbleDom: Using Scribble-Annotated Histology Images to Identify Domains in Spatial Transcriptomics Data.

Spatial domain identification is a very important problem in the field of Spatial Transcriptomics (ST). The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. In this paper, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network (CNN) based approach. Named ScribbleDom, our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot micro-environment through convolution filters of varying sizes, in the form of Inception blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in Adjusted Rand Index (ARI) for 9 of the 12 Human DLPFC samples, and 15.54% improvement in the Melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669). Supplementary data are available at Bioinformatics online.

Circadian rhythm disruptions associated with opioid use disorder in synaptic proteomes of human dorsolateral prefrontal cortex and nucleus accumbens

Opioid craving and relapse vulnerability is associated with severe and persistent sleep and circadian rhythm disruptions. Understanding the neurobiological underpinnings of circadian rhythms and opioid use disorder (OUD) may prove valuable for developing new treatments for opioid addiction. Previous work indicated molecular rhythm disruptions in the human brain associated with OUD, highlighting synaptic alterations in the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc)—key brain regions involved in cognition and reward, and heavily implicated in the pathophysiology of OUD. To provide further insights into the synaptic alterations in OUD, we used mass-spectrometry based proteomics to deeply profile protein expression alterations in bulk tissue and synaptosome preparations from DLPFC and NAc of unaffected and OUD subjects. We identified 55 differentially expressed (DE) proteins in DLPFC homogenates, and 44 DE proteins in NAc homogenates, between unaffected and OUD subjects. In synaptosomes, we identified 161 and 56 DE proteins in DLPFC and NAc, respectively, of OUD subjects. By comparing homogenate and synaptosome protein expression, we identified proteins enriched specifically in synapses that were significantly altered in both DLPFC and NAc of OUD subjects. Across brain regions, synaptic protein alterations in OUD subjects were primarily identified in glutamate, GABA, and circadian rhythm signaling. Using time-of-death (TOD) analyses, where the TOD of each subject is used as a time-point across a 24-h cycle, we were able to map circadian-related changes associated with OUD in synaptic proteomes associated with vesicle-mediated transport and membrane trafficking in the NAc and platelet-derived growth factor receptor beta signaling in DLPFC. Collectively, our findings lend further support for molecular rhythm disruptions in synaptic signaling in the human brain as a key factor in opioid addiction.

The Nature of Prefrontal Cortical GABA Neuron Alterations in Schizophrenia: Markedly Lower Somatostatin and Parvalbumin Gene Expression Without Missing Neurons.

In schizophrenia, somatostatin (SST) and parvalbumin (PV) mRNA levels are lower in the dorsolateral prefrontal cortex (DLPFC), but it remains unclear whether these findings reflect lower transcript levels per neuron, fewer neurons, or both. Distinguishing among these alternatives has implications for understanding the pathogenesis of, and developing new treatments for, DLPFC dysfunction in schizophrenia. To identify SST and PV neurons in postmortem human DLPFC, the authors used fluorescent in situ hybridization to label cells expressing two transcripts not altered in schizophrenia: vesicular GABA transporter (VGAT; a marker of all GABA neurons) and SOX6 (a marker of only SST and PV neurons). In cortical layers 2 and 4, where SST and PV neurons, respectively, are differentially enriched, levels of SST and PV mRNA per neuron and the relative densities of SST-, PV-, and VGAT/SOX6-positive neurons were quantified. In individuals with schizophrenia, mRNA levels per positive neuron were markedly and significantly lower for SST in both layers (effect sizes >1.48) and for PV only in layer 4 (effect size=1.14) relative to matched unaffected individuals. In contrast, the relative densities of all SST-, PV-, or VGAT/SOX6-positive neurons were unaltered in schizophrenia. Novel multiplex fluorescent in situ hybridization techniques permit definitive distinction between cellular levels of transcripts and the presence of neurons expressing those transcripts. In schizophrenia, pronounced SST and PV mRNA deficits are attributable to lower levels of each transcript per neuron, not fewer neurons, arguing against death or abnormal migration of these neurons. Instead, these neurons appear to be functionally altered and thus amenable to therapeutic interventions.