Probes for INS

Dietary lipids, particularly omega-3 polyunsaturated fatty acids, are speculated to impact behaviors linked to the dopaminergic system, such as movement and control of circadian rhythms. However, the ability to draw a direct link between dopaminergic omega-3 fatty acid metabolism and behavioral outcomes has been limited to the use of diet-based approaches, which are confounded by systemic effects. Here, neuronal lipid metabolism was targeted in a diet-independent manner by manipulation of long-chain acyl-CoA synthetase 6 (ACSL6) expression. ACSL6 performs the initial reaction for cellular fatty acid metabolism and prefers the omega-3 polyunsaturated fatty acid, docosahexaenoic acid (DHA). The loss of Acsl6 in mice (Acsl6-/- ) depletes neuronal membranes of DHA content and results in phenotypes linked to dopaminergic control, such as hyperlocomotion, impaired short-term spatial memory, and imbalances in dopamine neurochemistry. To investigate the role of dopaminergic ACSL6 on these outcomes, a dopaminergic neuron-specific ACSL6 knockout mouse was generated (Acsl6DA-/- ). Acsl6DA-/- mice demonstrated hyperlocomotion and imbalances in striatal dopamine neurochemistry. Circadian rhythms of both the Acsl6-/- and the Acsl6DA-/- mice were similar to control mice under basal conditions. However, upon light entrainment, a mimetic of jet lag, both the complete knockout of ACSL6 and the dopaminergic-neuron-specific loss of ACSL6 resulted in a longer recovery to entrainment compared to control mice. In conclusion, these data demonstrate that ACSL6 in dopaminergic neurons alters dopamine metabolism and regulation of light entrainment suggesting that DHA metabolism mediated by ACSL6 plays a role in dopamine neuron biology.

The development of multi-omic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. Here, we review recent biological insights gained from the use of scRNA-seq and ST, and the advantages of combining both, for profiling skin disease, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST for improving skin disease treatments and moving towards the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells' (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo-derived senescence signature (SenSig) using a foreign body response-driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and "cartilage-like" fibroblasts as senescent and defined cell type-specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.

BackgroundThe tumor microenvironment (TME) is highly complex, comprised of tumor cells, immune cells, stromal cells, and extracellular matrix. Understanding spatial interactions between various cell types and their activation states in the TME is crucial for implementing successful immunotherapy strategies against various types of cancer. This study demonstrates a highly sensitive and specific multiplexed technique, the RNAscope HiPlex v2 in situ hybridization (ISH) assay for spatial and transcriptomic profiling of target genes to assess immune regulation in human lung, breast, cervical and ovarian FFPE tumor tissues.MethodsWe have expanded our current RNAscope HiPlex assay capability of iteratively multiplexing up to 12 targets in fixed and fresh frozen samples to include formalin fixed paraffin embedded (FFPE) tissues. The novel FFPE reagent effectively reduces background autofluorescence, improving the signal to noise ratio. We have leveraged this technology to investigate spatial expression of 12 oncology and immuno-oncology target genes, including tumor markers, immune checkpoint markers, immunosuppression markers, immune cell markers and secreted chemokine RNA expression profile within the TME. The targets were simultaneously registered using HiPlex image registration software v2 that enables background subtraction.ResultsWe visualized T cell infiltration and identified T cell subsets within tumors using CD3and CD8 expression and activated T cells by IFNG expression. We further identified subsets of pro- and anti-inflammatory macrophages by CD68 and CD163 expression as well effector cells which secrete chemokines and cytokine. We also detected the hypoxia markers HIF1A and VEGF to elucidate the immunosuppressive state of tumor cells. Preliminary analysis and quantification of the HIF1A expression using HALO image analysis software showed higher copy numbers in the lung tumor as compared to the other tumors, demonstrating the sensitivity of the assay through differential expression. We additionally showed the differential expression of immune checkpoint markers PDCD1, and CD274 within the TME.ConclusionsUsing a highly sensitive multiplexed RNAscope HiPlex v2 ISH assay, we have demonstrated the capability of this technique to spatially resolve 12 targets in four different tumor types. The FFPE reagent efficiently quenched background autofluorescence in the tissues and identified immune cell signatures within the TME. Quantification of immunosuppressive markers further depicted a differential expression among various tumors. This technology is highly beneficial for investigating complex and spatial tumor-stroma interactions in basic science and translational research. The assay can also provide valuable understanding of the biological crosstalk among various cell types in complex and heterogeneous tissues.

A patient's genome, Van Eyk noted, contains information about that patient's disease predispositions and drug responses. She added, however, that better information about disease risks and drug responses could be gleaned from the proteome. Although there are only so many protein-encoding genes, the intricacies of protein expression generate various kinds of proteomic information in abundance. According to Van Eyk, information about disease-induced modifications, isoforms, concentration changes, and chemical complexity can inform predictions of what will happen in the body, in the context of the body and the environment. She suggests that a proteomics approach—one that would involve monitoring of not just one protein at a time, but thousands—could generate valuable clinical insights.

The last 5 y have seen the development and widespread adoption of high-plex spatial transcriptomic technology. This technique detects and quantifies mRNA transcripts in situ, meaning that transcriptomic signatures can be sampled from specific cells, structures, lesions, or anatomical regions while conserving the physical relationships that exist within complex tissues. These methods now frequently implement next-generation sequencing, enabling the simultaneous measurement of many targets, up to and including the whole mRNA transcriptome. To date, spatial transcriptomics has been foremost used in the fields of neuroscience and oncology, but there is potential for its use in transplantation sciences. Transplantation has a clear dependence on biopsies for diagnosis, monitoring, and research. Transplant patients represent a unique cohort with multiple organs of interest, clinical courses, demographics, and immunosuppressive regimens. Obtaining high complexity data on the disease processes underlying rejection, tolerance, infection, malignancy, and injury could identify new opportunities for therapeutic intervention and biomarker identification. In this review, we discuss currently available spatial transcriptomic technologies and how they can be applied to transplantation.

Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.