Researchers publishing in Aging Cell have used single-cell transcriptomics to discover new insights into how neural stem cells (NSCs) change with aging.
Adults do generate neurons
The adult brain does generate new neurons [1], particularly in the hippocampus, the part of the brain responsible for memory formation [2]. Neurogenesis is limited to very specific niches, however, and does not occur across the entire brain [3]. This is accomplished by NSCs, cells that can differentiate into neural progenitors (NPs), which can themselves differentiate into both neurons and astrocytes and have less ability to proliferate [4]. Astrocytes are helper cells that support neurons’ connections and metabolism [5].
NSCs are naturally heterogenous, coming from multiple different cell lineages even within a specific niche [6]. It has become clear that these lineage differences result in differences in function [7]. However, the composition and function of the various types of NSCs, as well as how they change over time, has not been fully elucidated.
By analyzing single-cell transcriptomics derived from multiple studies, these researchers aimed to help change that.
Normally quiescent and hard to identify
NSCs spend most of their time in a quiescent state [8], ready to be activated to replace losses; these researchers suggest that this is likely because they are so long-lived and need to protect themselves against the genetic damage associated with constant replication. However, their ongoing sleep requires energy, and natural processes often find them difficult to awaken [9]. This quiescence also makes them particularly difficult to analyze in many contexts, because instead of being identifiable by proliferation markers, they are normally noted by the absence of such markers instead.
Other markers are shared between cell types. For example, the transcription factor Sox2 (one of the four OSKM reprogramming factors) is expressed in NSCs but is also expressed in astrocytes. Similarly, the astrocyte marker GFAP has also been found in NSCs.
Other methods use morphology rather than biochemistry to identify types. Radial glia-like (RGL) cells have extensions and are slower to cycle than non-radial glia-like (NR) cells. Previous efforts to diffferentiate these types with a biomarker have been unsuccessful. RGL cells can be further divided into alpha and beta types; in younger animals, approximately three-quarters of RGL cells become the alpha type, which can differentiate into neurons and astrocytes, and the remaining quarter immediately become astrocytes. In older animals, however, the beta type predominates [10].
The researchers note that even with the fluorescent reporter proteins used in animal models, the wide variety of possible cell subtypes makes such tools exceptionally difficult to use for precise identification. Even previous attempts at transcriptomics looking for a molecular signature may have conflated astrocytes with RGLs [11].
Finding consistent signals
These researchers base their work on existing RNA sequencing datasets. They sought commonalities between seven different datasets; although the various research groups that created these datasets likely used the same sorts of tissue dissociation, they may have preprocessed their data or sequenced these cells in different ways. There were also differences between some of the animals used, with some studies using wild-type Black 6 mice and others using fluorescent reporters, and the brain regions analyzed were also different.
The team’s first analysis allowed them to readily group cells as being NSCs, NPs, and neuroblasts that form neurons. However, that same analysis also grouped together various other disparately named types of cells; the researchers suggest that this is a nomenclature issue and that the cells are, in fact, similar. One study’s NSCs was more similar to another study’s intermediate stage, while the former study’s cells labeled as astrocytes were determined to be similar to the latter study’s NSCs.
The researchers identified the expression of two genes common between all seven studies that represent NSCs, and another ten gene expressions that represent NPs. Some other genes were judged likely to specifically represent quiescent NSCs. Other genes represent activation, and the researchers expressed concern that some genes previously used to mark NPs were simply representing activated NSCs.
Their analysis linked NSCs to another gene, Ecrg4, whose deficiency was found to promote proliferation and improve cognition in mice [12]. Another linked gene, Tnc, promotes neurogenesis [13].
The link to aging
The exhaustion of NSCs is directly linked to the progressive loss of neurogenesis, and thus memory, with aging [14]. An examination of two studies found that the aging of NSCs begins rapidly, at only 4.5 months old in some mouse cells. This includes both an increase in inflammation along with epigenetic changes.
While senescent cells are highly heterogenous, NSCs become senescent just as many other types do, with increases in p16, p21, and p53 along with the well-known marker SA-β-gal. 83 genes related to the senescence-related SASP phenotype were analyzed, and the researchers found evidence that quiescent NSCs may become senescent or transform into astrocytes with aging. Just like with other cells, the proliferation of the SASP affects the neural stem cell niche [15]. Specifically, senescent NSCs express IL-33 [16] and IL-15 [17], factors that lead to neuroinflammation but may also lead to proliferation; however, further research is needed in this area.
The aged NSC niche was also characterized by a loss of communication. Chemical signals that are being sent in younger brains are not being sent in older ones. These signals were associated with crucial abilities, including the organization of synapses [18].
The researchers acknowledge that they have not fully elucidated all of the various subtypes of NSCs and how they differentiate. However, they may have uncovered useful targets in promoting NSC proliferation and limiting the effects of cellular senescence. They call for a more comprehensive analysis of cell types within the aging brain and the use of more advanced computing tools to gain a better handle on the signals sent by NSCs andhow and why they turn into other cells.
Literature
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