used an algorithm that reconstructed the 3D shape of

Here is a summary of some of their findings:

– The heritability of Alzheimer’s disease is complex and variable, depending on factors like age and population. Regarding age, in samples with older participants, there is a greater likelihood that AD genetic factors will play a role in disease risk.

– SNP-based heritability estimates decrease by 12% when APOE is excluded. When all other genome-wide significant hits were removed, SNP-based heritability only decreased by 1%.

– In APOE e44 carriers, the average age of onset is about 68 years, whereas for APOE e4 non-carriers, it is about 84 years. The authors suggest that for the APOE e4 non-carriers, disease burden is mostly due to the aggregate effect of a large number of common SNPs as well as comorbid disorders.

– When they restricted SNPs to a microglia gene set that was only 3% of SNPs, it still explained between 50% and 93% of the SNP-based heritability. This strongly suggests that microglia are critical for the mechanism of AD. It would have been nice to see this compared to gene sets for other cell types, such as neurons or oligodendrocytes, which could have given a better sense of how variability in the function of these different cell types may contribute to AD risk and also helped to assess the robustness of their methods.

– The authors suggest that for neurodegenerative disorders, heritability estimates should be adjusted for the age-related prevalence of cases. This would help to account for the genetic liability for the disease of individuals who do not yet show symptoms.

– The article provides helpful insights into the complex nature of Alzheimer’s disease heritability. It highlights the need for further research to identify biologically relevant AD gene-sets/pathways that could increase the signal-to-noise ratio by highlighting the most influential SNPs/genes in AD.

In this study, the authors used a Mendelian randomization approach to examine the causal relationships between various risk factors and Alzheimer’s disease. They used the UK Biobank, which has a massive sample size of >300,000 participants.

They found that genetic variation at one gene — APOE — is far and away the main mediator of Alzheimer’s disease genetic risk. This replicates why Alzheimer’s has been called a quasi-monogenic disease — APOE has that large of an effect.

They don’t hold anything back in the discussion, basically arguing that their study disagrees with observational studies because their methodology is better and observational studies are wrong, because observational studies can’t identify causality.

Instead, they suggest that associations with Alzheimer’s disease in observational studies are due to reverse causation (i.e. they are symptoms of early/prodromal Alzheimer’s disease, rather than causes) or simply due to selection bias.

This means that the things often associated with Alzheimer’s disease in observational studies — body mass index, blood pressure, and physical activity — might not actually increase the risk of disease. Interestingly, Alzheimer’s genetic risk in this study is actually associated with a lower body mass index and body fat in people 53-72 years old.

They also found that Alzheimer’s disease risk is associated with a lower fluid intelligence score, with no causal effect on educational attainment.

One caveat I have with the study is that I’d like to learn about the associations of these risk factors with other forms of cognitive impairment. Laypeople often use the term “Alzheimer’s” to refer to dementia or age-related cognitive impairment in general.

For example, does a higher genetic risk for elevated blood pressure or body mass index causally affect the risk for vascular-associated cognitive impairment? My guess is that they might, which might cause this study to be a bit misleading if the results were taken in the wrong way.

That said, there seems to be a real effect of APOE on the risk of cognitive impairment that is independent of classical risk factors body mass index, blood pressure, and physical activity. And this study helps to parse out how that might be occurring, which will hopefully help to develop better preventive approaches and treatments.

1. The protocol helps to overcome an impediment to correlative en bloc EM and IHC by enabling the ultrastructural preservation of brain tissue and antibody penetration into relatively thick tissue sections, without the use of permeabilizing agents. This could help to facilitate the use of pre-embedding IHC in ultrastructural analysis techniques such as 3D EM.

They replicate the finding that permeabilization dramatically decreases ultrastructural quality, so should be avoided if possible:

They argue that a key way they were able to accomplish antibody penetration without permeabilization was via the preservation of the extracellular space (ECS).

2. The brains sections they used are still quite thin relative to those that are practical in neuropathology, at 300 um – 1 mm. In neuropathology, human brains are somewhat frequently sectioned at 5 mm, but even that is challenging and requires expert skill.

One could try to use a device such as a compresstome to help with the sectioning process. Here’s a video showing how the compresstome works on mouse brains. But it seems difficult to scale this to human brain sizes.

(If one were to achieve such thin sections in a high-fidelity way, you could theoretically cryopreserve them with vitrification or near-vitrification procedures and therefore avoid fixation altogether. Although, avoiding fixatives would also make room temperature preservation not currently possible.)

3. Another possible mechanism for why their protocol worked, that the authors did not discuss as far as I could tell, is that tissue decomposition during the immersion fixation process — which is slower than perfusion fixation — may itself cause membrane permeabilization. With a long enough time period of decomposition, cell membrane breakdown is an inevitable event, so the question is really whether the immersion fixation was slow enough to allow it to occur. My guess is that it was a contributing factor.

This may also help to explain why some epitopes are more accessible (eg Homer) than others (eg PSD-95). If a protein is a part of stronger gel-like networks, this gel-like network will likely break down slower during the decomposition process, and therefore be more difficult for antibodies to access without permeabilization.

4. Do we even need immersion fixation for ECS preservation? They cite Cragg 1980 as an example of a study that achieved ECS preservation using perfusion. It’s still not entirely clear to me why perfusion doesn’t usually achieve ECS preservation, but it seems like it probably depends on the osmotic concentration of the perfusate. Cragg 1980 is 30+ years old now; it would be ideal if it could be replicated and the phenomenon understood better.

This study furthers our understanding of a syndrome of accelerated aging. The authors show a significant acceleration of an epigenetic aging marker in the blood of people with Down syndrome. Furthermore, they show that this effect is present at birth and is significantly stronger in newborns who have Down syndrome plus GATA1 mutations. This association with GATA1 mutations is intriguing as GATA1 mutations are associated with transient abnormal myelopoiesis. One thing that this study does not do is investigate the mechanism by which this age acceleration occurs.

One hypothesis based on this finding is that it might help explain why people with Down syndrome have an increased susceptibility to Alzheimer’s disease. Lore has long been that this is due to the triplication of amyloid precursor protein, however, this study suggests that age acceleration may also play at least a part in the increased susceptibility of people with Down syndrome to aging-associated cognitive impairment and Alzheimer-type neuropathology.

The authors studied hippocampal CA1 and olfactory bulb circuits via multiple imaging modalities, including 2-photon calcium imaging, X-ray microscopy, and serial block-face electron microscopy. In all cases, the imaging modalities had different strengths in identifying different circuit elements, and the authors were able to correlate structure and function in interesting ways. The interplay between structural, functional, and molecular-level data will be increasingly critical in systems neuroscience, and this study highlights some important points.

The authors should be commended on showing that X-ray microscopy can be used without causing significant damage on fixed and osmium/uranium/lead en-bloc EM embedded tissue, which is an important advance. The authors also showed that X-ray microscopy can be used at high resolution on thick mammalian brain tissues; this is important because X-ray microscopy has the potential to provide structural details at the level of individual dendrites, which is possible with volume electron microscopy but less easily scalable. Finally, the authors point out that staining protein and lipid distributions defines the ultrastructure of the tissue; this is an important point that is often missed.

Diffusion is a type of passive transport that involves the net movement of molecules or ions from an area of higher concentration to an area of lower concentration down a concentration gradient. The concentration gradient is the difference in concentration between two points.

In biology, diffusion plays an important role in many biological events such as molecular transport, cell signaling, and neurotransmitter movement across a synaptic cleft.

How far would a typical molecule diffuse in a millisecond? A second? An hour?

Diffusion is a description of how molecules will randomly move around in a liquid. Their movement will be limited if they hit a barrier or randomly collide with another molecule and react, which is not described by diffusion.

The distance a molecule will diffuse in a certain amount of time depends on the size of the molecule, the viscosity of the fluid, and the temperature.

This can be explained by the Stokes-Einstein relation: D = kT/(6πηa), where:

– D = the diffusion constant

– k = the Boltzmann constant

– T = the temperature

– η = the viscosity coefficient of the fluid

– a = the radius of the diffusing molecule

The constant value is 6 assuming that the radius of the diffusing molecule is greater than the radius of the solvent.

Assuming that we are talking about diffusion at 25° C and in water, then there is a nice calculator on physiologyweb.com that lists diffusion coefficients for different ions and molecules:

If we are talking about the diffusion of a small molecule neurotransmitter such as glutamate, it has a MW of 147, which is close to glucose’s MW of 180. So we can use glucose’s diffusion coefficient as a rough guide for the diffusion of some types of small molecule neurotransmitters.

This calculator suggests that glucose will diffuse 1000 nm in a millisecond, 31,000 nm (31 μm) in a second, or 1,900,000 nm (1.9 mm) in an hour.

Molecular diffusion rates are helpful when building intuition about what structural information is necessary to be able to infer in brain preservation. Because, in the way that I think about it, molecular events that occur more slowly than rapid long-term memory recall can be instantiated (which, conservatively, can occur in ~500-1000 ms) cannot be uniquely necessary for the structural information describing it.

Inspired by CalTech’s Question #21 for cognitive scientists: “What is diffusion? How far would a typical molecule diffuse in a millisecond? A second? An hour? How does the diffusion equation differ from the cable equation?”