Expanding cell-to-cell interactions

High-dimensional single-cell technologies have revolutionized the study of cell biology by unraveling the cellular heterogeneity and molecular complexity that underlies human health and disease. Most recently, these techniques have been used to answer intriguing questions about the cells of the central nervous system (CNS). For example, this approach was used to describe a protective type of microglia present in the brains of patients with Alzheimer's disease ([ 1 ][1]) and an astrocyte population that represses protective antioxidant and anti-inflammatory transcriptional programs in a neuroinflammatory mouse model and in multiple sclerosis (MS) patients ([ 2 ][2]). On page 360 of this issue, Clark et al. ([ 3 ][3]) take current single-cell technologies to the next level by dissecting cell-to-cell cross-talk in the CNS and beyond. Our knowledge of the immune system in particular has been taken to unprecedented levels by the use of single-cell approaches, most notably single-cell RNA-sequencing, but these findings have generated their own questions and challenges. Among the most pressing is the need to precisely map the cross-talk between newly identified cell populations in vivo in real time and to understand their relative locations within a tissue in both homeostatic and disease states. Deciphering these key phenomena at the single-cell level will be critical to our understanding of molecular disease pathophysiology and will likely underpin the development of the next generation of therapeutics for a wide range of conditions. Traditionally, cell-to-cell interactions have been studied by using sorted bulk cell populations that were cultured together in vitro and then underwent bulk RNA-sequencing. The resulting gene expression data were then “mined” with available algorithms to predict possible interactions. However, using bulk cell populations often dismisses the sometimes critical contribution of minor subpopulations within the whole, and the in vitro environment excludes the importance of the physiological niche in which cells are intimately in contact. Additionally, spatial transcriptomics can now combine, for example, techniques such as the use of photoactivatable fluorescent reporters with single-cell RNA-sequencing (NICHE-seq) to infer the cellular and molecular composition of immunological niches ([ 4 ][4]). However, until now, no high-throughput technological pipeline that can comprehensively profile cell-to-cell interactions at the single-cell level within tissues has been available. Understanding the intercellular interactions that shape and maintain the CNS is particularly challenging because these tissues are relatively inaccessible for experimental manipulations or difficult to model in vitro, and a small subset of cells within them may mediate highly potent effects. Thus, the cellular cross-talk that shapes the responses of specific cell types within the brain remains unknown, particularly during neuroinflammation, which is observed in many brain diseases. This knowledge gap drove the development of rabies barcode interaction detection followed by sequencing (RABID-seq) by Clark et al. , which combines molecular barcoding, viral tracing, and single-cell RNA-sequencing in vivo in an unbiased manner, expanding the toolkit for analysis of cell-to-cell interactions at the single-cell level. The authors investigated astrocyte-interacting cells, enabling the reconstruction of both interaction and transcriptional maps in vivo, all at single-cell resolution. Microglia-astrocyte interactions are important during CNS development, homeostasis, and diseases such as MS ([ 5 ][5], [ 6 ][6]), Alzheimer's disease ([ 7 ][7]), and prion disease ([ 8 ][8]). However, a comprehensive understanding of these interactions and how they shift during neuroinflammation is lacking. Using the RABID-seq approach, Clark et al. compared the interactions of astrocytes in mice with induced experimental autoimmune encephalomyelitis (EAE), which may model human MS. The authors demonstrated the specific interaction between microglia and astrocytes but also detected astrocyte-astrocyte interactions, as well as interactions with microglia and other cells in the steady state and at the peak of EAE in affected mice. In the context of EAE, the authors uncovered astrocyte interaction networks that were more diverse than anticipated and included interactions with peripheral cells recruited to the inflamed CNS such as T cells, as well as with known interaction partners such as microglia. ![Figure][9] Microglia-astrocyte cross-talk Microglia-astrocyte cross-talk in EAE is revealed by RABID-seq. Microglia SEMA4D and EFNB3 interact with PLXNB2 and EPHB3 on astrocytes. Activation of SEMA4D-PLXNB2 and EFNB3-EPHB3 signaling induces the expression of proinflammatory factors (IL-6, TNF, IL-1β, NOS2) by both cell populations. GRAPHIC: V. ALTOUNIAN/ SCIENCE This study revealed that microglia-astrocyte cross-talk was modified during EAE, becoming a bidirectional interaction that induced a local proinflammatory environment. RABID-seq allowed the authors to track specifically those microglia that were in close contact with astrocytes, enabling the identification of substantial differences in gene expression between microglia and astrocytes from naïve versus EAE mice, which revealed important details of the pathways implicated in their interactions. Further pathway and gene expression analyses suggested two main axes of interactions that promoted CNS inflammation in EAE: microglia-astrocyte semaphorin 4D (SEMA4D)–plexin B2 (PLXNB2) signaling and microglial Ephrin-B3 (EFNB3) and astrocyte ephrin type-B receptor 3 (EPHB3) (see the figure), pathways already implicated in the process of inflammation in various diseases. The authors validated the roles of this receptor signaling in the process of CNS inflammation during EAE. Inactivation of the receptor Sema4d in microglia or its ligand Plxnb2 in astrocytes resulted in EAE amelioration, whereas ablation of Ephb3 expression in astrocytes and Efnb3 in microglia led to reduced recruitment of proinflammatory monocytes to the CNS during EAE. These results demonstrate that EPHB3 receptor signaling boosts the proinflammatory activities of astrocytes, whereas EFNB3 signaling boosts nuclear factor–κB (NF-κB)–driven responses in microglia during EAE. Thus, EPHB3 signaling in astrocytes promotes CNS inflammation and is a candidate target for therapeutic intervention. Looking to MS, the authors analyzed a publicly available RNA-sequencing dataset of brain samples from MS patients and matched controls and found evidence of relatively increased interactions between microglia and astrocytes through the SEMA4D-PLXNB2 axis in patients. Moreover, immunolabeling revealed higher expression of SEMA4D in microglia and PLXNB2 in astrocytes of MS patients, underlining the relevance of this work for human MS. Together, the study of Clark et al. presents a new approach (RABID-seq) that allows scientists to recreate small networks of in vivo single cell–to–single cell interactions more accurately and efficiently than before. Although the technique currently does not formally incorporate spatial information, it could be modified to do so using “seqFISH,” a technique that uses fluorescent RNA probes, allowing visualization of cell localization in a tissue expressing a particular RNA ([ 9 ][10]). In addition, although the assessment of cell interactions itself is highly time efficient once the model is established, at present each research team wishing to use the approach will need to generate the appropriate mouse models. 1. [↵][11]1. H. Keren-Shaul et al ., Cell 169, 1276 (2017). [OpenUrl][12][CrossRef][13][PubMed][14] 2. [↵][15]1. M. A. Wheeler et al ., Nature 578, 593 (2020). [OpenUrl][16][CrossRef][17][PubMed][18] 3. [↵][19]1. I. C. Clark et al ., Science 372, eabf1230 (2021). [OpenUrl][20][Abstract/FREE Full Text][21] 4. [↵][22]1. C. Medaglia et al ., Science 358, 1622 (2017). [OpenUrl][23][Abstract/FREE Full Text][24] 5. [↵][25]1. V. Rothhammer et al ., Nature 557, 724 (2018). 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