Single-nucleus sequencing technology is a technique developed at the single-cell level. It allows for direct nuclei extraction and capture from frozen samples while still preserving the high resolution of single-cell sequencing. This technology can accurately distinguish cellular heterogeneity and reveal the gene structure and gene expression status of individual cells. Due to its high sample inclusivity, the advent of single-nucleus sequencing has solved the problem of being unable to perform single-cell experiments on certain precious frozen samples and irregularly shaped cells. It aids clinical treatment and diagnosis in fields such as cardiopulmonary diseases and neurodevelopment, accelerating the era of precision medicine.

1.Extensive Nuclei Sequencing Experience:Years of experimental accumulation; unique nuclei preparation protocols for various tissue types including heart, brain, liver parenchyma, muscle, and fat, effectively improving nuclei preparation efficiency, with a nuclei capture rate as high as 65%–80%.
2.Strict Quality Control:Lieven implements strict quality control throughout the entire process, with full monitoring of the experimental workflow. Reverse transcription and library preparation have been automated, reducing human interference, ensuring customers obtain high-quality data and analysis results, and providing comprehensive support and solutions.
3.Proven Track Record:Extensive nuclei sequencing experience, having contributed to the publication of multiple high-impact SCI papers.

Sample Types:
1.Frozen tissues, cultured cell lines, etc.; typically a piece the size of a soybean.
2.Stored in a -80°C freezer; shipped on dry ice.

Experimental Groups:High social rank Controls, n = 7; Low social rank Depressive Like, n = 7; Low social rank RES, n = 5.
Primary Technical Methods:Single - nucleus RNA sequencing (snRNA - seq); Spatial Transcriptome (Visium ST) sequencing.
Single - Cell Capture Platform:10x Genomics.
Single - Cell Analysis Tools Applied:Cell clustering analysis, subCluster analysis, differential gene expression analysis, GO/Pathway analysis, CellphoneDB analysis, QuSAGE analysis, WGCNA analysis.

According to World Health Organization (WHO) data, since 2017, Major Depressive Disorder (MDD) has become one of the leading causes of disability worldwide. Preliminary exploration of the underlying cellular and molecular mechanisms of MDD in males at single-cell resolution has been conducted (Corina Nagy, Nat Neurosci, 2020).

This study is the first to employ single-nucleus RNA sequencing (snRNA-Seq) and spatial transcriptomics technologies, using a primate model, to investigate the mechanisms underlying depression-like phenotypes influenced by social status in females, and to identify associated genes and cell types.

Researchers used behavioral quantitative evaluation indicators to classify monkey social hierarchy and psychological state. Single-nucleus sequencing and spatial omics sequencing were performed on the three groups of samples with different clinical manifestations. To ensure the rigor and reliability of subsequent analyses, researchers, together with Lieven's bioinformatics team, performed quality control on the data based on gene counts, mitochondrial genes, and doublets. Ultimately, 136,231 high-quality nuclei were obtained. Sequencing quality comparison between groups showed no significant differences, indicating stable and reliable nuclear sequencing with minimal batch effects, suitable for subsequent analysis. These nuclei were finally classified into 8 major cell types. The proportional characteristics of neuronal cells to glial cells were consistent with previous studies, further demonstrating data reliability.

The analysis team then used classic single-cell analysis strategies in the neuroscience field to compare differentially expressed genes (DEGs) in major cell types between the DL group and the normal group, identifying a total of 240 non-redundant DEGs. These DEGs were primarily enriched in glial cells, especially microglia (56.25%), rather than in neuronal cells. The specific differences in microglia were closely related to the activation of immune responses.
Subsequently, the research team innovatively used co-expression network analysis (Co - Exp) on these genes, combined with the DisGeNET database and MDD research, to identify two major disease-related gene patterns: the D-Pattern (significantly associated with depression, primarily composed of microglia) and the R-Pattern (associated with social status, primarily composed of astrocytes). This provided the basis for researchers and the analysis team to focus on microglia.

Subsequently, the analysis team employed the traditional single-cell sequencing strategy of first classifying major types followed by sub-clustering to perform re-clustering (Sub-clustering) on microglia. Among the 8 subpopulations, the Mic03 subpopulation showed a high correlation with the depressive sample phenotype. The consistency of this subpopulation with microglial differential genes exceeded that of other groups, and it expressed IQGAP2, FYN, PDE7A, and ARHGEF3, which may regulate microglial activation and neuroinflammation. Based on Qusage functional enrichment analysis, it was found to have strong pro-inflammatory characteristics, ultimately naming it "pro-inflammatory microglia in depression-like phenotype" (PIMID).

Different from traditional spatial transcriptomics analysis strategies, the researchers and Lieven's analysis team used WGCNA to analyze the spatial transcriptomics data, revealing modules associated with depressive behavior, positive, and negative emotional behaviors, showing differences in gene spatial distribution. Through bioinformatics analysis, these differences were localized. The relationships between cell types, regions, and behaviors are illustrated in the figure below (figure not translated).

PIMID (Darkturquoise Module) was primarily localized to Layer 6 of the monkey brain anatomy, corresponding to area 3 of the macaque dlPFC ST region. The localization of PIMID in human and macaque dlPFC cortical space may facilitate targeted intervention for depressive phenotypes and provides a foundation for subsequent research.

Based on a low-rank depressive monkey model, this team discovered that gene expression changes related to depression-like behaviors primarily occur in microglia, and reported a pro-inflammatory microglial subpopulation enriched under depression-like conditions. These research findings provide potential new targets for precise intervention in depression and serve as a reference for research in this field.

Link to Original Article:https://doi.org/10.1038/s41593-023-01379-4