Accurate inference of genome-wide spatial expression with iSpatial

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning

The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 CNS datasets covering 4 species and 10 CNS diseases. Leveraging these strengths, we successfully integrate the collected human CNS datasets into a large-scale reference to annotate cell types and subtypes in neural tissues. Results demonstrate that scCM provides an accurate annotation, along with rich spatial information of cell state. In summary, scCM is a robust and promising method for integrating large-scale CNS scRNA-seq data, enabling researchers to gain insights into the cellular and molecular mechanisms underlying CNS functions and diseases.

MIF as a potential diagnostic and prognostic biomarker for triple-negative breast cancer that correlates with the polarization of M2 macrophages

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a crucial role in antitumor immunity. However, the role of MIF in influencing the tumor microenvironment (TME) and prognosis of triple-negative breast cancer (TNBC) remains to be elucidated. Using R, we analyzed single-cell RNA sequencing (scRNA-seq) data of 41 567 cells from 10 TNBC tumor samples and spatial transcriptomic data from two patients. Relationships between MIF expression and immune cell infiltration, clinicopathological stage, and survival prognosis were determined using samples from The Cancer Genome Atlas (TCGA) and validated in a clinical cohort using immunohistochemistry. Analysis of scRNA-seq data revealed that MIF secreted by epithelial cells in TNBC patients could regulate the polarization of macrophages into the M2 phenotype, which plays a key role in modulating the TME. Spatial transcriptomic data also showed that epithelial cells (tumor cells) and MIF were proximally located. Analysis of TCGA samples confirmed that tumor tissues of patients with high MIF expression were enriched with M2 macrophages and showed a higher T stage. High MIF expression was significantly associated with poor patient prognosis. Immunohistochemical staining showed high MIF expression was associated with younger patients and worse clinicopathological staging. MIF secreted by epithelial cells may represent a potential biomarker for the diagnosis and prognosis of TNBC and may promote TNBC invasion by remodeling the tumor immune microenvironment.

A molecularly defined NAcSh D1 subtype controls feeding and energy homeostasis

Orchestrating complex behavioral states, such as approach and consumption of food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also plays an important role in controlling appetite and satiety in responses to changing external stimuli. However, the specific neuronal subtypes of NAc involved as well as how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here, we deciphered the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and defined a dopamine receptor D1(Drd1)- and Serpinb2-expressing subtype located in NAcSh encoding food consumption. Chemogenetics- and optogenetics-mediated regulation of Serpinb2 + neurons bidirectionally regulates food seeking and consumption specifically. Circuitry stimulation revealed the NAcSh Serpinb2 →LH LepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2 + neuron ablation reduces food intake and upregulates energy expenditure resulting in body weight loss. Together, our study reveals a neural circuit consisted of molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, which can serve as a potential therapeutic target for eating disorders.

STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning

Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.

Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain

The prefrontal cortex (PFC) is a complex brain region that regulates diverse functions ranging from cognition, emotion and executive action to even pain processing. To decode the cellular and circuit organization of such diverse functions, we employed spatially resolved single-cell transcriptome profiling of the adult mouse PFC. Results revealed that PFC has distinct cell-type composition and gene-expression patterns relative to neighboring cortical areas-with neuronal excitability-regulating genes differently expressed. These cellular and molecular features are further segregated within PFC subregions, alluding to the subregion-specificity of several PFC functions. PFC projects to major subcortical targets through combinations of neuronal subtypes, which emerge in a target-intrinsic fashion. Finally, based on these features, we identified distinct cell types and circuits in PFC underlying chronic pain, an escalating healthcare challenge with limited molecular understanding. Collectively, this comprehensive map will facilitate decoding of discrete molecular, cellular and circuit mechanisms underlying specific PFC functions in health and disease.