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Calafate S, Özturan G, Thrupp N, Vanderlinden J, Santa-Marinha L, Morais-Ribeiro R, Ruggiero A, Bozic I, Rusterholz T, Lorente-Echeverría B, Dias M, Chen WT, Fiers M, Lu A, Vlaeminck I, Creemers E, Craessaerts K, Vandenbempt J, van Boekholdt L, Poovathingal S, Davie K, Thal DR, Wierda K, Oliveira TG, Slutsky I, Adamantidis A, De Strooper B, de Wit J. Early alterations in the MCH system link aberrant neuronal activity and sleep disturbances in a mouse model of Alzheimer's disease. Nat Neurosci. 2023 Jun;26(6):1021-1031. Epub 2023 May 15 PubMed.
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NYU
This is another beautiful example of how sequencing-based spatial transcriptomics, despite its relatively low resolution, can be used to generate, or test, hypotheses. Here, the platform was used to identify differentially expressed genes in specific anatomical domains. Because their spatial transcriptomics platform is sequencing-based, the authors can unbiasedly identify differentially expressed genes using standard bioinformatic tests on differential gene expression, here edgeR. They use it to show that the pyramidal layer of CA1 is enriched in genes associated with synaptic scaling or homeostatic plasticity in the APPNL-G-F mouse model of Alzheimer’s disease.
This paper is also an outstanding example of how publicly available datasets can be reanalyzed to uncover new biological insights. The sheer amount of data generated in each spatial transcriptomics and single-cell RNA-Seq experiment in laboratories around the world are just waiting for the right question to be asked and the right tools to be applied.
Currently, scientists have to pick between two spatial transcriptomics approaches: either they are sequencing-based, and therefore allow for the unbiased discovery of regionally restricted differentially expressed genes, but don’t offer single-cell resolution (for example 10xGenomics Visium); alternatively, they can use spatial transcriptomics platforms that allow for single-cell resolution but require a set of candidate genes as input, and are therefore restricted to a limited number of known cell, subtype, or substate markers (for example, Vizgen’s MERFISH).
I am looking forward to seeing spatial transcriptomics applied to other diseases that are anatomically restricted or have a focal pathology, such as stroke, spinal cord injury, or stab wound insults. Currently, its most powerful application is in combination with other sequencing modalities, such as single-cell RNA-Seq. Integration across the modalities truly uncovers how regionally-restricted insults affect the local environment at the single-cell level and can be used as a powerful discovery tool in neurobiology.
View all comments by Philip HaselUniversity of Pennsylvania
The authors are to be congratulated on their overall approach to define mechanisms underlying early abnormalities in Alzheimer's disease by using an amyloid precursor protein (APP) mutant knock-in model and then defining alterations that temporally coincide with impaired responses. Transgenic overexpression in select neuronal populations may miss important changes that occur when the mutations are under the endogenous promoter.
This particular model, the APPKI NL-G-F mouse, has aggressive plaque accumulation at the time when hippocampal hyperactivity was found, which is consistent with the findings of Marc Aurel Busche, showing greater excitability near plaques (Busche et al., 2008).
Here, the authors have not defined a critical role for MCH in Alzheimer's dysfunction/synapse loss, etc. An important step would be to see whether inhibition of MCH neurons, or loss of MCH, alters the temporal progression of hippocampal dysfunction, which may best be observed in a model with slower progression, e.g., in the APP knock-in NL-F model.
Sleep/wake abnormalities that are typically seen early in Alzheimer's disease include insomnia (poor consolidation of nighttime sleep or longer awakenings at night) along with alterations in sleep state-dependent waveforms (slow wave power, spindle amplitude and theta synchrony), and in REM sleep increased postural muscle activity (REM sleep behavior disorder). Loss of REM sleep may be a later finding in advanced Alzheimer's disease.
Sleep abnormalities observed in the present study in the APPKI NL-G-F mice, at an age when amyloid plaques are abundant, did not necessarily emulate sleep findings in mild cognitive impairment or Alzheimer's disease. Rather, differences were limited to reduced REM sleep and impaired homeostatic response to sleep loss. These findings are not consistent with sleep-wake responses to MCH neuro lesioning, which does not alter REM sleep amount but tends to shift more of the total REM sleep to the dark period (normal active/increased wake time for nocturnal mice).
These differences in responses suggest injury to other behavioral state-dependent groups of neurons (cholinergic, noradrenergic, etc., in the APPKI NL-G-F model), which would also be consistent with the work of Lea Grinberg and others showing that many behavioral, state-dependent neuronal groups are injured in Alzheimer's disease (Oh et al., 2022).
It will now be important to go back to the APPKI NL-G-F mouse and define temporal loss of/injury to these other neuromodulatory groups of neurons. A differential sensitivity to injury in this model could also provide clues to the molecular mechanisms of neural injury in Alzheimer's.
References:
Busche MA, Eichhoff G, Adelsberger H, Abramowski D, Wiederhold KH, Haass C, Staufenbiel M, Konnerth A, Garaschuk O. Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer's disease. Science. 2008 Sep 19;321(5896):1686-9. PubMed.
Oh JY, Walsh CM, Ranasinghe K, Mladinov M, Pereira FL, Petersen C, Falgàs N, Yack L, Lamore T, Nasar R, Lew C, Li S, Metzler T, Coppola Q, Pandher N, Le M, Heuer HW, Heinsen H, Spina S, Seeley WW, Kramer J, Rabinovici GD, Boxer AL, Miller BL, Vossel K, Neylan TC, Grinberg LT. Subcortical Neuronal Correlates of Sleep in Neurodegenerative Diseases. JAMA Neurol. 2022 May 1;79(5):498-508. PubMed.
View all comments by Sigrid VeaseyMake a Comment
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