Recent developments in single‐cell transcriptomics have opened new opportunities for studying dynamic processes in immunology in a high throughput and unbiased manner. Starting from a mixture of ...cells in different stages of a developmental process, unsupervised trajectory inference algorithms aim to automatically reconstruct the underlying developmental path that cells are following. In this review, we break down the strategies used by this novel class of methods, and organize their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods. We also give an overview of new insights these methods have already provided regarding the wiring and gene regulation of cell differentiation. As the trajectory inference field is still in its infancy, we propose several future developments that will ultimately lead to a global and data‐driven way of studying immune cell differentiation.
Trajectory inference is a novel category of computational tools which aim to construct accurate models of dynamic processes, given the expression data of single cells sampled uniformly from the biological process of interest. This review takes a reductionist approach to analyze and categorize state‐of‐the‐art trajectory inference methods.
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SUMMARY
Single‐cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high‐dimensional catalogs of millions of cells, including ...genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single‐cell omics, especially single‐cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well‐executed benchmarking studies to highlight advances made through the application of single‐cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single‐cell sequencing with revolutionary perspectives in plant biology.
Significance Statement
In this review, we introduce the current technical advances in single‐cell omics, especially single‐cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well‐executed benchmarking studies to highlight advances made through the application of single‐cell omics techniques.
Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA ...sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.
Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial ...perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies.
Synopsis
The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.
CPA can be trained on highly multiplexed, single‐cell experiments with thousands of conditions to predict unmeasured phenotypes (e.g., specific dose responses).
It can generalize to predict responses to small molecules never seen in the training by adding priors on chemical space.
Validations using a newly generated combinatorial drug perturbation dataset demonstrate the accuracy of CPA in predicting unseen drug combinations.
CPA is also applicable to genetic combinatorial screens, as shown by imputing in silico 5,329 missing combinations in a single‐cell perturb‐seq experiment with diverse genetic interactions.
The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.
Fibroblasts, custodians of tissue architecture and function, are no longer considered a monolithic entity across tissues and disease indications. Recent advances in single‐cell technologies provide ...an unrestricted, high‐resolution view of fibroblast heterogeneity that exists within and across tissues. In this review, we summarize a compendium of single‐cell transcriptomic studies and provide a comprehensive accounting of fibroblast subsets, many of which have been described to occupy specific niches in tissues at homeostatic and pathologic states. Understanding this heterogeneity is particularly important in the context of cancer, as the diverse cancer‐associated fibroblast (CAF) phenotypes in the tumor microenvironment (TME) are directly impacted by the expression phenotypes of their predecessors. Relationships between these heterogeneous populations often accompany and influence response to therapy in cancer and fibrosis. We further highlight the importance of integrating single‐cell studies to deduce common fibroblast phenotypes across disease states, which will facilitate the identification of common signaling pathways, gene regulatory programs, and cell surface markers that are going to advance drug discovery and targeting.
FASEB: the mechanism of plant development Alique, Daniel; Gómez‐Felipe, Andrea; Kuhn, André ...
The New phytologist,
12/2023, Letnik:
240, Številka:
5
Journal Article
Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the ...biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.
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•Minimal residual diseases in melanoma exhibit cellular and spatial heterogeneity•Cell-state transition contributes to co-emergence of distinct drug-tolerant states•RXR signaling drives emergence of a cell population conferring treatment resistance•Targeting RXR signaling is promising for delaying or obviating relapse in melanoma
Drug-tolerant cells that persist through treatment of melanoma exhibit multiple transcriptional states, one of which is a key driver that can be targeted therapeutically.
Background
Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully ...understood.
Methods
Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single‐cell analysis, we analyzed the single‐cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full‐thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.
Results
The single‐cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1‐expressing fibroblasts in the leukocyte‐infiltrated areas in AD skin. CCR7‐expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand–receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1‐expressing fibroblasts, CCL13‐ and CCL18‐expressing M2 macrophages, CCR7‐ and LAMP3‐expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.
Conclusion
In this study, we show the unknown cellular crosstalk in leukocyte‐infiltrated area in lesional skin. Our findings provide a comprehensive in‐depth knowledge of the nature of AD skin lesions to guide the development of better treatments.
Single‐cell RNA‐ and spatial RNA‐seq identified complex cellular interactionsin lesional skin of AD. COL18A1+ fibroblasts express COL6A5, COL4A1, TNC, and CCL19 and interact with CCR7 positive LAMP3+ dendritic cells and T cells. CCL13+ and CCL18+ M2 macrophages show an interaction with T cells by MRC1‐PTPRC and CD14‐ITGB1/2. The serum level of TNC and CCL18 shows a positive correlation with AD severity. Abbreviations: AD, atopic dermatitis; CCL, C–C motif chemokine ligand; CCR, C–C motif chemokine receptor; COL, collagen; ITGB, integrin beta; LAMP3, lysosome‐associated membrane glycoprotein 3; MRC, mannose receptor C; PTPRC, protein tyrosine phosphatase receptor; TNC, Tenascin C
The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to ...create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse and were grouped by developmental anatomical units and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission, and membrane conductance. We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system and enables genetic manipulation of specific cell types.
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•Systematic survey of transcriptomic cell types in the mouse nervous system•Taxonomy and hierarchical organization of molecular cell types•Seven distinct astrocyte types with regionally restricted distribution•Neuronal diversity is similar across brain regions
Single-cell transcriptional profiling of the adult mouse nervous system uncovers new cell classes and types across regions, providing a clearer picture of cell diversity by region and a reference atlas for studying the mammalian nervous system.
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