Probes for INS

Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.

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.

Interoception, the ability to timely and precisely sense changes inside the body, is critical for survival1-4. Vagal sensory neurons (VSNs) form an important body-to-brain connection, navigating visceral organs along the rostral-caudal axis of the body and crossing the surface-lumen axis of organs into appropriate tissue layers5,6. The brain can discriminate numerous body signals through VSNs, but the underlying coding strategy remains poorly understood. Here we show that VSNs code visceral organ, tissue layer and stimulus modality-three key features of an interoceptive signal-in different dimensions. Large-scale single-cell profiling of VSNs from seven major organs in mice using multiplexed projection barcodes reveals a 'visceral organ' dimension composed of differentially expressed gene modules that code organs along the body's rostral-caudal axis. We discover another 'tissue layer' dimension with gene modules that code the locations of VSN endings along the surface-lumen axis of organs. Using calcium-imaging-guided spatial transcriptomics, we show that VSNs are organized into functional units to sense similar stimuli across organs and tissue layers; this constitutes a third 'stimulus modality' dimension. The three independent feature-coding dimensions together specify many parallel VSN pathways in a combinatorial manner and facilitate the complex projection of VSNs in the brainstem. Our study highlights a multidimensional coding architecture of the mammalian vagal interoceptive system for effective signal communication.

Adaptive behaviors arise from an integration of current sensory context and internal representations of past experiences. The central amygdala (CeA) is positioned as a key integrator of cognitive and affective signals, yet it remains unknown whether individual populations simultaneously carry current- and future-state representations. We find that a primary nociceptive population within the CeA of mice, defined by CGRP-receptor (Calcrl) expression, receives topographic sensory information, with spatially defined representations of internal and external stimuli. While Calcrl+ neurons in both the rostral and caudal CeA respond to noxious stimuli, rostral neurons promote locomotor responses to externally sourced threats, while caudal CeA Calcrl+ neurons are activated by internal threats and promote passive coping behaviors and associative valence coding. During associative fear learning, rostral CeA Calcrl+ neurons stably encode noxious stimulus occurrence, while caudal CeA Calcrl+ neurons acquire predictive responses. This arrangement supports valence-aligned representations of current and future threats for the generation of adaptive behaviors.

Spatial gene expression, achieved classically through _in situ_ hybridization, is a fundamental tool for topographic phenotyping of cell types in the nervous system. Newly developed techniques allow for the visualization of multiple mRNAs at single-cell resolution, greatly expanding the ability to link gene expression to tissue topography. Yet, methods for efficient and accurate quantification and analysis of high dimensional _in situ_- hybridization are limited. To this end, the Single-Cell Automated Multiplex Pipeline for RNA (SCAMPR) was developed, facilitating rapid and accurate segmentation of neuronal cell bodies using a dual immunohistochemistry-RNAscope protocol and quantification of low and high abundance mRNA signals using open-source image processing and automated segmentation tools. Proof of principle using SCAMPR focused on spatial mapping of gene expression by peripheral (vagal nodose) and central (visual cortex) neurons. The analytical effectiveness of SCAMPR is demonstrated by identifying the impact of early life stress on differential gene expression by vagal neuron subtypes.

Abstract Unavailable