Abstract Breast cancer is a heterogenous disease that can be classified into multiple subtypes including the most aggressive basal-like and triple-negative subtypes. Understanding the heterogeneity within the normal mammary basal epithelial cells holds the key to inform us about basal-like cancer cell differentiation dynamics as well as potential cells of origin. Although it is known that the mammary basal compartment contains small pools of stem cells that fuel normal tissue morphogenesis and regeneration, a comprehensive yet focused analysis of the transcriptional makeup of the basal cells is lacking. We used single-cell RNA-sequencing and multiplexed RNA in-situ hybridization to characterize mammary basal cell heterogeneity. We used bioinformatic and computational pipelines to characterize the molecular features as well as predict differentiation dynamics and cell–cell communications of the newly identified basal cell states. We used genetic cell labeling to map the in vivo fates of cells in one of these states. We identified four major distinct transcriptional states within the mammary basal cells that exhibit gene expression signatures suggestive of different functional activity and metabolic preference. Our in vivo labeling and ex vivo organoid culture data suggest that one of these states, marked by Egr2 expression, represents a dynamic transcriptional state that all basal cells transit through during pubertal mammary morphogenesis. Our study provides a systematic approach to understanding the molecular heterogeneity of mammary basal cells and identifies previously unknown dynamics of basal cell transcriptional states.
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This content will become publicly available on June 1, 2024
Single-cell Ca 2+ parameter inference reveals how transcriptional states inform dynamic cell responses
Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca2+dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca2+signalling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca2+dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signalling dynamics in single cells.
more » « less- Award ID(s):
- 2045327
- NSF-PAR ID:
- 10492147
- Publisher / Repository:
- Royal Society
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 20
- Issue:
- 203
- ISSN:
- 1742-5662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Emerging RNA viruses that target the central nervous system (CNS) lead to cognitive sequelae in survivors. Studies in humans and mice infected with West Nile virus (WNV), a re-emerging RNA virus associated with learning and memory deficits, revealed microglial-mediated synapse elimination within the hippocampus. Moreover, CNS-resident memory T (TRM) cells activate microglia, limiting synapse recovery and inducing spatial learning defects in WNV-recovered mice. The signals involved in T cell-microglia interactions are unknown.
Methods Here, we examined immune cells within the murine WNV-recovered forebrain using single-cell RNA sequencing to identify putative ligand-receptor pairs involved in intercellular communication between T cells and microglia. Clustering and differential gene analyses were followed by protein validation and genetic and antibody-based approaches utilizing an established murine model of WNV recovery in which microglia and complement promote ongoing hippocampal synaptic loss.
Results Profiling of host transcriptome immune cells at 25 days post-infection in mice revealed a shift in forebrain homeostatic microglia to activated subpopulations with transcriptional signatures that have previously been observed in studies of neurodegenerative diseases. Importantly, CXCL16/CXCR6, a chemokine signaling pathway involved in TRM cell biology, was identified as critically regulating CXCR6 expressing CD8+TRM cell numbers within the WNV-recovered forebrain. We demonstrate that CXCL16 is highly expressed by all myeloid cells, and its unique receptor, CXCR6, is highly expressed on all CD8+T cells. Using genetic and pharmacological approaches, we demonstrate that CXCL16/CXCR6 not only is required for the maintenance of WNV-specific CD8 TRM cells in the post-infectious CNS, but also contributes to their expression of TRM cell markers. Moreover, CXCR6+CD8+T cells are required for glial activation and ongoing synapse elimination.
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NPR1 was regulated by AtSR1 binding to a CGCG box in theNPR1 promotor. Theatsr1 mutant exhibited resistance to the virulent strain ofPseudomonas syringae pv.tomato (Pst ), however, was susceptible to an avirulentPst strain carryingavrRpt2 , due to the failure of the induction of hypersensitive responses. These resistant/susceptible phenotypes in theatsr1 mutant were reversed in thenpr1 mutant background, suggesting that AtSR1 regulates NPR1 as a downstream target during plant immune response. The virulentPst strain triggered a transient elevation in intracellular Ca2+concentration, whereas the avirulentPst strain triggered a prolonged change. The distinct Ca2+signatures were decoded into the regulation of NPR1 expression through AtSR1's IQ motif binding with Ca2+‐free‐CaM2, while AtSR1's calmodulin‐binding domain with Ca2+‐bound‐CaM2. These observations reveal a role for AtSR1 as a Ca2+‐mediated transcription regulator in controlling the NPR1‐mediated plant immune response. -
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Abstract Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.
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Signaling Pathways > Cell Fate Signaling
Nervous System Development > Vertebrates: Regional Development
