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

Science, Journal Year: 2024, Volume and Issue: 384(6698)

Published: May 23, 2024

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

Language: Английский

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The human and non-human primate developmental GTEx projects

Nature, Journal Year: 2025, Volume and Issue: 637(8046), P. 557 - 564

Published: Jan. 15, 2025

Language: Английский

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Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex

Genome biology, Journal Year: 2025, Volume and Issue: 26(1)

Published: April 7, 2025

Language: Английский

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Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders

Cell, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Abstract Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS revealed the as significant contributor to disease. However, prioritizing causal common and rare human disease, understanding how selective pressures have shaped genome, remains challenge. Here, we predicted effects of 15 million with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts adult fetal brain heart, producing nearly two context-specific predictions. Using these predictions, distinguish candidate underlying traits diseases their effects. While variant are more cell-type-specific, exert cell-type-shared regulatory effects, particularly targeting affecting neurons. To prioritize de novo mutations extreme developed FLARE, functional genomic model constraint. FLARE outperformed other methods case from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate potential integrating maps population genetics learning-based effect prediction elucidate mechanisms development disease–ultimately, supporting notion genetic contributions neurodevelopmental disorders predominantly rare.

Language: Английский

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Topic modeling analysis of the Allen Human Brain Atlas

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 26, 2025

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It composed of many regions that exhibit specific distributions cell types distinct patterns functional connections. This complexity rooted in differential transcription. constituent different express distinctive combinations genes as they develop mature, ultimately shaping their state adulthood. How precisely the genetic information anatomical structures connected to underlying biological functions remains an open question modern neuroscience. A major challenge identification "universal patterns", which do not depend on particular individual, but are instead basic structural properties shared by brains. Despite vast amount gene expression data available at both bulk single-cell levels, this task challenging, mainly due lack suitable mining tools. In paper, we propose approach address issue based hierarchical version Stochastic Block Modeling. Thanks its choice priors, method particularly effective identifying these universal features. We use laboratory test our algorithm dataset obtained from six independent brains Allen Human Brain Atlas. show proposed indeed able identify much better than more traditional algorithms such Latent Dirichlet Allocation or Weighted Correlation Network Analysis. probabilistic association between samples find well represents known organization. Moreover, leveraging peculiar "fuzzy" sets with method, examples transcriptional post-transcriptional pathways associated regions, highlighting potential approach.

Language: Английский

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Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

Multi-trait QTL (xQTL) colocalization has shown great promises in identifying causal variants with shared genetic etiology across multiple molecular modalities, contexts, and complex diseases. However, the lack of scalable efficient methods to integrate large-scale multi-omics data limits deeper insights into xQTL regulation. Here, we propose ColocBoost , a multi-task learning method that can scale hundreds traits, while accounting for within genomic region interest. employs specialized gradient boosting framework adaptively couple colocalized traits performing variant selection, thereby enhancing detection weaker signals compared existing pairwise multi-trait methods. We applied genome-wide 17 gene-level single-nucleus bulk from aging brain cortex ROSMAP individuals (average N = 595), encompassing 6 cell types, 3 regions modalities (expression, splicing, protein abundance). Across xQTLs, identified 16,503 distinct events, exhibiting 10.7(± 0.74)-fold enrichment heritability 57 diseases/traits showing strong concordance element-gene pairs validated by CRISPR screening assays. When against Alzheimer's disease (AD) GWAS, up 2.5-fold more loci, explaining twice AD fine-mapping without integration. This improvement is largely attributable 's enhanced sensitivity detecting gene-distal colocalizations, as supported known enhancer-gene links, highlighting its ability identify biologically plausible susceptibility loci underlying regulatory mechanisms. Notably, several genes including BLNK CTSH showed sub-threshold associations but were through colocalizations which provide new functional support their involvement pathogenesis.

Language: Английский

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Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 12, 2024

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference is an important strategy for estimating type composition in heterogeneous tissues, such as human brain. Computational methods have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq immunohistochemistry data. A major limitation developing improved algorithms has the lack integrated datasets with orthogonal measurements gene expression estimates proportions on same tissue sample. Deconvolution algorithm performance not yet evaluated across different RNA extraction (cytosolic, nuclear, whole RNA), library preparation types (mRNA enrichment vs. ribosomal depletion), matched datasets. rich multi-assay dataset was generated postmortem dorsolateral prefrontal cortex (DLPFC) from 22 blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, (across six library/extraction combinations), RNAScope/Immunofluorescence (RNAScope/IF) broad types. The Mean Ratio method, implemented DeconvoBuddies R package, selecting marker genes. Six computational were DLPFC predicted compared to RNAScope/IF measurements. Bisque hspe most accurate methods, robust differences extractions. This showed that size differences, genes differentially quantified libraries, variability snRNA-seq impact accuracy current methods.

Language: Английский

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Private information leakage from single-cell count matrices

Cell, Journal Year: 2024, Volume and Issue: 187(23), P. 6537 - 6549.e10

Published: Oct. 2, 2024

Language: Английский

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Deciphering Schizophrenia and Neurodegenerative Mechanisms Through Gene Coexpression Networks Modelling in L2/3 IT Neurons

IFMBE proceedings, Journal Year: 2025, Volume and Issue: unknown, P. 195 - 206

Published: Jan. 1, 2025

Language: Английский

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