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Real unipolar neurons do indeed exist in human?

Real unipolar neurons do indeed exist in human?


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I studied that the unipolar neuron in human body are not really unipolar but they're pseoudounipolar neurons. On the other hand according to what I understood from wikipedia (neuron) there are real unipolar neuron.

In fact, I'm confused (if unipolar cell does exist in human body or doesn't) and I would like get you help about it.


Single-Cell Detection of Secreted Aβ and sAPPα from Human IPSC-Derived Neurons and Astrocytes

Secreted factors play a central role in normal and pathological processes in every tissue in the body. The brain is composed of a highly complex milieu of different cell types and few methods exist that can identify which individual cells in a complex mixture are secreting specific analytes. By identifying which cells are responsible, we can better understand neural physiology and pathophysiology, more readily identify the underlying pathways responsible for analyte production, and ultimately use this information to guide the development of novel therapeutic strategies that target the cell types of relevance. We present here a method for detecting analytes secreted from single human induced pluripotent stem cell (iPSC)-derived neural cells and have applied the method to measure amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα), analytes central to Alzheimer's disease pathogenesis. Through these studies, we have uncovered the dynamic range of secretion profiles of these analytes from single iPSC-derived neuronal and glial cells and have molecularly characterized subpopulations of these cells through immunostaining and gene expression analyses. In examining Aβ and sAPPα secretion from single cells, we were able to identify previously unappreciated complexities in the biology of APP cleavage that could not otherwise have been found by studying averaged responses over pools of cells. This technique can be readily adapted to the detection of other analytes secreted by neural cells, which would have the potential to open new perspectives into human CNS development and dysfunction.

SIGNIFICANCE STATEMENT We have established a technology that, for the first time, detects secreted analytes from single human neurons and astrocytes. We examine secretion of the Alzheimer's disease-relevant factors amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα) and present novel findings that could not have been observed without a single-cell analytical platform. First, we identify a previously unappreciated subpopulation that secretes high levels of Aβ in the absence of detectable sAPPα. Further, we show that multiple cell types secrete high levels of Aβ and sAPPα, but cells expressing GABAergic neuronal markers are overrepresented. Finally, we show that astrocytes are competent to secrete high levels of Aβ and therefore may be a significant contributor to Aβ accumulation in the brain.


Reality is virtual

That, evolutionary game theory predicts, is what evolution has done for us. Natural selection has given us sensory systems that are a simplifying user interface for the complexity of the world. Space, as we perceive it around us, is a 3D computer desktop, with tables, chairs, the moon and mountains icons within it.

In other words, our senses constitute a virtual reality. If you play the video game Grand Theft Auto with a virtual-reality add-on, you see a 3D world with 3D objects, such as a black steering wheel in front of you. If you turn your head, however, the steering wheel disappears. Indeed, it ceases to exist, because it only exists when we are looking where it should be in the simulation. The reality that exists&mdashcircuits and software again&mdashis utterly unlike a steering wheel. But it prompts you to create a steering wheel when it is needed, and to destroy it when it isn&rsquot.

Our senses tell us only what we need to know to survive
art by Rudi Sebastian

In like manner, we create an apple when we look, and destroy it when we look away. Something exists when we don&rsquot look, but it isn&rsquot an apple, and is probably nothing like an apple. The human perception of an apple is a data structure that indicates something edible (a fitness pay-off) and how to eat it. We create these data structures with a glance, and erase them with a blink. Physical objects, and indeed the space and time they exist in, are evolution&rsquos way of presenting fitness pay-offs in a compact and usable form.

But hang on, drop the apple. A lion on the African savannah isn&rsquot just an icon in your interface. It has agency, and can kill you, so it must be objectively real.

I wouldn&rsquot mess with a lion, for the same reason I wouldn&rsquot carelessly drag the green icon of my novel to the virtual recycle bin. Not because I take that icon literally, and think the novel is green and rectangular. But I do take that icon seriously: if I drag it to the bin, I could lose all my work.

The objection that a lion must be objectively real because anyone who looks over there sees a lion that we can all agree looks like a lion&mdashso it isn&rsquot unique to our subjective experience&mdashisn&rsquot a valid one, either. Humans agree about what we see because we have all evolved a similar interface. The interfaces of some other species, such as prey mammals, may have icons for lions that are similar to ours, and that guide actions similar to ours, such as keeping far away from them.

That leaves the fact that treating our observed, subjective reality as objective reality has allowed us to create scientific theories&mdashframeworks that allow us to make predictions about how the world works, and so are presumably part of an objective reality that exists outside our heads. But here too there are hints from deep within science itself that perception and reality don&rsquot match.

Quantum theory is our best physical theory of fundamental reality. But with its counter-intuitive effects of &ldquospooky action at a distance&rdquo and the perennial mystery of the dead-yet-alive Schrödinger&rsquos cat, it drives a coach and horses through cherished ideas from our classical realm of experience: that objects have definite values of the properties pertaining to them, that those properties don&rsquot depend on how they are observed, and that influences propagate no faster than light.

This is jolting if we assume that objects and their measurable properties are part of an objective reality. But it is no surprise if we think of objects and their properties as data structures created as needed to represent fitness pay-offs. In this case, the values of properties will depend on when and how we create them.

This approach aligns with the quantum-Bayesian interpretation of quantum theory, or QBism, in which the uncertainty inherent in quantum observations is all in the minds of the observers. As three pioneers of QBism, Christopher Fuchs, David Mermin and Rüdiger Schack have put it, &ldquoA measurement does not, as the term unfortunately suggests, reveal a pre-existing state of affairs. It is an action on the world by an agent that results in the creation of an outcome&mdasha new experience for that agent. &lsquoIntervention&rsquo might be a better term.&rdquo

If our team&rsquos evolutionary ideas are true, that might lend momentum to models of quantum theory that see quantum states, and the mathematical and interpretational structures around them, as &ldquoepistemic&rdquo&mdashreflecting not necessarily reality, but just our state of knowledge of it.

But it goes further. Even perceptions as seemingly fundamental as space and time might not actually be part of objective reality. That insight could inform our search for theories that unite the two great theories at the heart of modern physics.

&ldquoEven perceptions as fundamental as space and time might not be part of objective reality&rdquo

For decades, we have tried and failed to reconcile quantum theory with general relativity, Einstein&rsquos theory of gravity that dictates how the universe works on large scales. At a very basic level, these theories fail to agree on the nature of space and time.

General relativity demands that space-time, the four-dimensional structure that space and time together form, is smooth and continuous, whereas a quantum description requires a pixelated description. As the theoretical physicist Nima Arkani-Hamed has said: &ldquoAlmost all of us believe that space-time doesn&rsquot exist, that space-time is doomed, and has to be replaced by some more primitive building blocks.&rdquo Admittedly, no one yet knows what those might be&mdashbut our insights suggest the hunch they must be replaced is right.

It isn&rsquot just in physics where we may need to overhaul our ideas about reality to make progress. Another is in solving the &ldquohard problem&rdquo of consciousness. This problem of how and why our brains generate conscious experience remains intractable despite centuries of thought. As biologist Thomas Huxley put it in 1869: &ldquoHow it is that anything so remarkable as a state of consciousness comes about as a result of irritating nervous tissue, is just as unaccountable as the appearance of the djinn, when Aladdin rubbed his lamp.&rdquo

The brain-exciting technology of transcranial magnetic stimulation (TMS) illustrates how little progress we have made. Suppose we place a TMS unit near your scalp, on the right side of your head, near an area of the occipital cortex called V4. We turn on the device, and its strong and focused magnetic fields inhibit neural activity nearby. All colour drains away from the left half of your visual world you see only shades of grey. We turn off the device, and the colour seeps back in.

artwork by Marina Loeb


The Little Brain in the Heart

Dr. Andrew Armour of the UCLA Neurocardiology Research Center discovered a sophisticated collection of neurons in the heart that organized into a small, complex nervous system. The heart’s nervous system contains around 40,000 neurons called sensory neurites that communicate with the brain. Dr. Armour dubbed this discovery as the "little brain in the heart." Memory is a distributive process which means you can’t localize it to a neuron or a group of neurons in the brain. The memory itself is distributed throughout the neural system. So, why do we draw a line at the brain? Maybe it&aposs time we distinguish the functions of the brain and what we call the mind.

What Is the Mind?

The mind is considered the center of human consciousness. Scientists have always tried to describe it as a consequence of brain functions. The brain was always considered to be the primary hardware. However, an increasing amount of evidence suggests that the mind is a sophisticated software that goes beyond the physical limits of our skulls.

The Mind Is Not Located Solely in the Brain

A quote from the late Dr. Candace Pert, a pharmacologist at Georgetown University explains the strange transplant experiences. "The mind is not just in the brain, but also exists throughout the body. The mind and body communicate with each other through chemicals known as peptides. These peptides are found in the brain as well as in the stomach, in muscles and in all of our major organs. I believe that memory can be accessed anywhere in the peptide/receptor network. For instance, a memory associated with food may be linked to the pancreas or liver and such associations can be transplanted from one person to another."

Professor Dan Siegel of UCLA School of Medicine describes the mind as, “the emergent self-organizing process, both embodied and relational, that regulates energy and information flow within and among us.” This definition supports the claim that the mind extends beyond our brains. Siegel takes it a step further. He believes that the mind extends into a some space outside of our bodies. He argues that the mind is our perception of life and life itself. That means that it’s difficult to separate our personal view of the world from our interactions.


Results

Isolation of an infectious SARS-CoV-2 virus

We isolated SARS-CoV-2 (SARS-CoV-2 NRW-42) from a nasopharyngeal and oropharyngeal swab specimen of an infected patient admitted to our university hospital, University of Düsseldorf (see Materials and Methods section for culturing and propagation). To investigate whether SARS-CoV-2 replicates in inoculated African green monkey kidney cells (Vero CCL-81), we performed real-time quantitative polymerase chain reaction (qPCR) analysis with cell culture supernatant. The amount of SARS-CoV-2 RNA drastically increased from 0-dpi until 3-dpi (Appendix Fig S1A). Next, we analyzed the infectivity of generated SARS-CoV-2 particles by propagating virus-containing supernatant to yet uninfected Vero cells. We confirmed the infection of new Vero cells by the emergence of virus-induced cytopathic effects (CPEs) and an increase in SARS-CoV-2 RNA over 4-dpi. The sequence (access number PRJNA627229 at the European Nucleotide Archive and the Sample accession number for NRW-42 which is SRS6522060) showed only eight nucleotide exchanges compared to SARS-CoV-2 Wuhan-Hu-1 isolate.

Isolation and validation of COVID-19 convalescent serum to detect SARS-CoV-2 infection

As of April 1, 2020, we could not procure commercial antibodies that can specifically determine SARS-CoV-2 infection. Therefore, we isolated COVID-19 convalescent serum and tested if they can specifically recognize SARS-CoV-2 infections in our experiments. We obtained blood samples of four independent individuals who recently recovered from COVID-19 (AB1, AB2, AB3, and AB4). Testing them in an enzyme-linked immunosorbent assay (ELISA) that used the SARS-CoV-2 S1 domain of the spike protein as an antigen revealed that, except for AB2, the rest of the convalescent serum contained SARS-CoV-2-specific IgG (Appendix Fig S1B). We then affinity-purified the convalescent serum against the full length ORF of SARS-CoV-2-N (see Materials and Methods section). In Western blots, which used extracts of brain organoids and Vero cells exposed to SARS-CoV-2, the antibodies affinity-purified from convalescent serum specifically recognized a signal similar to the size of the nucleoprotein of SARS-CoV-2. The recombinant SARS-CoV-2-N serves as a positive control in this experiment (Appendix Fig S1C). The convalescent serum AB4 also specifically recognized SARS-CoV-2-infected Vero cells. To further validate the specificity of the AB4, we performed co-immunostaining with a mouse monoclonal anti-SARS-CoV-2 S and a polyclonal anti-SARS-CoV-2 NP. As expected, all of these antibodies recognized only the SARS-CoV-2-infected Vero cells (Appendix Fig S2A). Similarly, AB4 could specifically recognize somas of SARS-CoV-2-positive cells in SARS-CoV-2 exposed brain organoids which were further labeled by the monoclonal anti-SARS-CoV-2 S antibody (Appendix Fig S2B). In Western blots that used SARS-CoV-2-exposed organoid extracts, both AB4 and mouse monoclonal antibodies recognized protein bands around 50 and 180 kDs, sizes similar to the nucleoprotein and uncleaved spike proteins Together, these experiments validate that AB4 detects SARS-CoV-2 infection (Appendix Fig S2C).

SARS-CoV-2 targets neurons of human brain organoids

Before we infected our 3D human brain organoids with the new SARS-CoV-2 NRW-42 isolate, we first tested if our experimental conditions are suitable to infect the well-studied ciliated human respiratory epithelial cells (hRECs), an apparent target for the SARS-CoV-2 (Lamers et al, 2020 ). We noticed that SARS-CoV-2 readily targets hRECs within 2 days of virus exposure (Fig 1A). We then tested if SARS-CoV-2 could infect 3D human brain organoids. To do this, we adapted our previously described protocol and differentiated brain organoids from two different iPSC lines (Donor 1, IMR90 and Donor 2, Crx-iPS Gabriel et al, 2017 ). In brief, we started with 10,000 iPSCs and induced differentiation into neural epithelium directly using SB431542 and dorsomorphin, the TGF beta and BMP4 inhibitors, respectively. Our differentiation condition did not also include an exogenous addition of retinoic acid, which could activate retinoic acid receptors (RAR) and induce an aberrant neuronal differentiation (Janesick et al, 2015 Gabriel et al, 2016 , 2017 Gabriel & Gopalakrishnan, 2017 ). As this method skips embryoid bodies formation, it reduces the heterogeneity in organoid sizes simultaneously avoiding the formation of mesoderm and endoderm, which are not required for ectodermal differentiation at early stages of differentiation (Streit et al, 2000 ). As described before, organoids exhibit their specific neuronal cell types, which are spatially restricted. The ventricular zone (VZ) harbors proliferating neural progenitors cells (NPCs) that display typically elongated nuclei which align to form a lumen, a neural tube-like structure. Cortical neurons are positioned basally to the VZ, forming a cortical plate (Fig 1B) (Lancaster & Knoblich, 2014 Giandomenico & Lancaster, 2017 Gopalakrishnan, 2019 ).

Figure 1. SARS-CoV-2 targets the cortical region of human brain organoids

  • A. A positive control experiment. SARS-CoV-2 readily targets ciliated human respiratory epithelial cells (hRECs). Acetylated α-tubulin labels cilia. Arrows point SARS-CoV-2-positive cells labeled by AB4 (green). Figures display scale bars. Bar diagram at right quantifies frequencies of SARS-CoV-2-positive cells in hRECs. At least six hREC sections from three (n = 3) independent samples were examined. Data presented as mean ± SEM.
  • B. Mock organoids of two age groups Day-15 (i) and-60 (ii) display typical cytoarchitecture of brain organoids. L, lumen, VZ, ventricular Zone is containing compact and palisade-like elongated nuclei of neural progenitor cells (NPCs, blue) and CP, a cortical plate containing TUJ-1-positive neurons (magenta). Note a distinct difference TUJ-1 labeling pattern between younger (Day-15) and older (Day-60) brain organoid. Figures display scale bars. Representative images from eight organoids cultured in at least three independent batches (n = 3) derived from donor-1 (IMR90) iPSC line.
  • C. Compared to mock organoids (i), SARS-CoV-2-exposed Day-15 organoids display SARS-CoV-2-positive cells (AB4, green) in their outer periphery, a region of the cortical plate (ii) that is specified by TUJ-1-positive neurons (magenta). L, the lumen of a VZ, the inner area of an organoid where NPCs are located, is free from SARS-CoV-2-positive cells. Magnified region (dotted while box) is given below. At least 10 organoids from five different batches (n = 5) are tested. Figures display scale bars.
  • D. SARS-CoV-2-exposed Day-60 organoids. Compared to Day 15 organoids and mock (i), Day-60 organoids display an increased number of SARS-CoV-2-positive cells (AB4, green) in their cortical plate that is specified by TUJ-1-positive neurons (magenta) (ii). Magnified region (dotted while box) is given below, showing the perinuclear location of SARS-CoV-2 in cortical neurons. At least 10 organoids from five different batches (n = 5) are tested. Figures display scale bars.
  • E. The bar diagram quantifies frequencies of SARS-CoV-2-positive cells in different brain organoid sections derived from two donor iPSC lines (IMR90 and Crx-iPS, see Materials and Methods). Please note that each point represents one organoid section. SARS-CoV-2 shows an enhanced tropism for Day-60 organoids. Note, comparative statistics are shown between different age groups and respective days post-infection (dpi) of organoids, and the significance is given as Asterisks in Day-60 groups. There is no significant difference in SARS-CoV-2-positive cells between 2- and 4-dpi within each age groups. At least twelve organoids sections from four (n = 4) independent batches, from each donor and day post-infections (dpi), were analyzed. One-way ANOVA, followed by Tukey's multiple comparisons test, ***P < 0.001. Data presented as mean ± SEM.
  • F. Subcellular localization SARS-CoV-2 in cortical neurons. High-resolution imaging and deconvolution show perinuclear localization of SARS-CoV-2. SARS-CoV-2 (AB4, green) and nucleus (gray). Figures display scale bars. Representative images from at least 200 cells are examined. White line surrounds perinuclear border, and red line encircles the nucleus.
  • G. Determination of viral progeny. Supernatants of SARS-CoV-2 exposed Vero cells, and brain organoids were analyzed for viral RNA assessed by qRT–PCR. While an increase in viral RNA was detected in the supernatants of Vero cells, no apparent increase was identified in brain organoid supernatants. Data are obtained from five technical replicates from four (n = 4) independent batches of organoids. Data presented as mean ± SEM.

We exposed at least two different age groups of organoids (Day-15 and Day-60) to SARS-CoV-2 (TCID50/ml of 50 which is equivalent to 17.5 PFU/organoid, see Materials and Methods section for details) and analyzed after 2 and 4 days post-infection (dpi). First, we began analyzing Day-15 organoids, a developmental stage used to study ZIKV infections (Gabriel et al, 2017 ). At this developmental stage, organoids mostly constitute actively proliferating NPCs at the VZs and a primitive cortical plate containing fewer early neurons (Fig 1B). Testing the target cell types of SARS-CoV-2 in these organoids revealed that SARS-CoV-2 could mostly target the cortical plate specified by pan-neuronal marker TUJ-1 that is spatially distinct from the VZ (Fig 1C and Appendix Fig S2D). To exclude the possibility that the virus may have a limited capacity of diffusion to target NPCs at the inner part of the intact 3D organoids, we directly exposed NPCs’ 2D cultures to SARS-CoV-2. Compared to 2D cortical neuronal cultures, NPCs cultures displayed only fewer cells positive for SARS-CoV-2. These findings indicate that SARS-CoV-2 has a preferred tropism to neurons, as reported recently (preprint: Mesci et al, 2020 preprint: Song et al, 2020 Yang et al, 2020a ) (Appendix Fig S3A). This is indeed in striking contrast to ZIKV, which directly targets NPCs present at the inner region of brain organoids and triggers them to prematurely differentiate into neurons leading to congenital microcephaly (Cugola et al, 2016 Qian et al, 2016 Gabriel et al, 2017 ).

Analyzing the cortical regions of Day-60 organoids revealed that the number of SARS-CoV-2-positive cells was significantly higher than in Day-15 organoids. This suggests that SARS-CoV-2 prefers relatively mature neuronal cell types present in older organoids (Fig. 1D and E). Day-60 organoids indeed displayed signs of maturation as judged by more MAP2-positive neurons, S100β-positive astrocytes, and fewer Iba-1-positive microglial cells (Appendix Fig S3B–D). Importantly, the perinuclear localization of SARS-CoV-2 in somas of cortical neurons is similar to the virus's localization pattern in Vero cells, indicating that SARS-CoV-2 can enter into neuronal cells of brain organoids (Fig 1F). Turning our analysis to the later time point of infection (dpi-4 and dpi-6) revealed no apparent increase in SARS-CoV-2-positive cells although dpi-6 organoids exhibited a slightly compromised integrity (Fig 1E and Appendix Fig S4A). Corroborating to this, we could not detect an increase in viral RNA in the supernatants between 2- and 4-dpi (Fig 1G). In contrast to brain organoids, SARS-CoV-2 productively infects vascular, kidney, and gut organoids (Lamers et al, 2020 Monteil et al, 2020 Zhou et al, 2020 ). Notably, angiotensin-converting enzyme 2 (ACE-2), an entry receptor of SARS-CoV-2, is highly expressed in these organoid types. Testing the ACE-2 expression at the mRNA level via a qRT–PCR revealed that both iPSCs-derived brain organoids and neurons exhibited

12.5- and 50-fold lesser than human respiratory epithelial cells (hREC), which served as a positive control (Appendix Fig S4B). Our Western blots using anti-ACE2 antibodies recognized ACE2 in organoid extracts only at higher exposure conditions (Appendix Fig S4C).

Since SARS-CoV-2 appears to preferably target neurons, we wondered if SARS-CoV-2 could productively replicate when exposed to an abundant number of mature neurons. To test this, we cultured organotypic slices of 60-day-old organoids, an alternative organoid culturing method that enhances neuronal maturation and viability. These cultures exhibit neuronal outgrowths as long-range axonal fibers expressing mature neuronal markers of MAP2, Tau, synapsin-1, and PSD95 (Gabriel et al, 2016 Giandomenico et al, 2019 Goranci-Buzhala et al, 2020 ). After directly exposing these slices to SARS-CoV-2, we detected the virus localized at the cell bodies of the neurons which are labeled by MAP2 and Tau (Appendix Fig S5A and B). We noticed only a slight increase in SARS-CoV-2 RNA within 2 days of viral exposure (Appendix Fig S5C). These experiments demonstrate that SARS-CoV-2 enters neurons of brain organoids but does not actively replicate.

SARS-CoV-2-positive neurons reveal aberrant Tau localization

Next, we identified that the SARS-CoV-2-positive region of the cortical plate is further substantiated by Tau, a microtubule-associated protein that stabilizes neuronal microtubules and promotes axonal growth (Fig 2) (Wang & Mandelkow, 2016 ). Tau dysfunction is implicated in Alzheimer's disease (AD) and other Tauopathies. Post-translational modifications in Tau, in particular, phosphorylations, modulate the ability of Tau to bind and assemble microtubules. In Tauopathies, Tau is aberrantly phosphorylated (hyperphosphorylation Cho & Johnson, 2004 Cohen et al, 2011 Castellani & Perry, 2019 ). A recent report showed that herpes simplex virus type 1 can induce AD-like effects, including hyperphosphorylation of Tau in 3D human brain-like tissue model (Cairns et al, 2020 ). This prompted us to investigate if SARS-CoV-2 has a consequence upon its entry into neurons.

Figure 2. SARS-CoV-2 deregulates of Tau in cortical neurons

  • A. Tau immunoreactivity (magenta) specifies the cortical plate (CP) surrounding the lumen (L) (i). Selected optic sections at high magnification (ii and iii) and high-resolution imaging (iv) show Tau localization only in axons of cortical neurons. Note the somas of neurons are free from Tau protein. At least eight organoids from four different batches (n = 4) are tested. Figures display scale bars.
  • B. Tau localization in SARS-CoV-2-positive neurons (AB4, green) in selected optic sections (i). Note, in contrast, to control groups, SARS-CoV-2-exposed organoids display mislocalized Tau (magenta) majorly into the somas of neurons (arrowheads). Selected confocal slices are shown to distinguish Tau mislocalization into neuronal soma (arrowheads). At high magnification, neuronal soma is further specified by the perinuclear localization of SARS-CoV-2 (green) (ii and iii). Bar diagrams at right quantifies the percentage of neurons (Mock and SARS-CoV-2 exposed) exhibiting mislocalized Tau (iv) and the fraction of SARS-CoV-2-positive neurons exhibiting Tau-positive axons spanning different cortical areas (v). For statistics, at least 300 cells from six organoids from four different batches (n = 4) were tested. Figures display scale bars. Unpaired t-test with Welch's correction, ***P < 0.001. Data presented as mean ± SD.
  • C. Schematic cartoon of differential Tau distribution in mock compared to SARS-CoV-2-positive neurons. In mock, Tau is sorted mainly to axons. In SARS-CoV-2-positive neurons, Tau is missorted to the soma (determined by Pan-Tau antibody). Furthermore, phosphorylated Tau (at T231) majorly localizes in the soma (bottom panel, determined by Tau AT-180 antibody, see below).
  • D. In contrast to controls (i), Tau AT180 antibody (magenta) that specifically recognizes the phosphorylated Threonine 231 of Tau protein distinctly localizes at the somas of SARS-CoV-2-positive neurons (AB4, green) (ii). At least four organoids from two different batches (n = 2) are tested. Figures display scale bars.
  • E. Co-localization of SARS-CoV-2 (AB4, green) and phosphorylated Tau protein (magenta) at somas of cortical neurons revealed by high-resolution imaging and deconvolution. Representative images from at least 300 cells examined. Figures display scale bars.
  • F. The bar diagram quantifies the fraction of Tau AT180-positive neurons that co-localize with SARS-CoV-2-positive neurons. For statistics, at least 250 cells from four organoids and two independent batches (n = 2) were examined. Unpaired t-test with Welch's correction. Data presented as mean ± SEM.

Under physiological conditions, Tau is mainly an axonal protein that localizes at the axons of mature neurons (Fig 2Ai–iv). Applying high-resolution imaging followed by deconvolution, we could visualize Tau's localization (as probed by a Pan-Tau antibody Tau5A6) exclusively in axons of the cortical neurons (Fig 2Av). The term Tau “missorting” is used when Tau protein is mislocalized into a cell soma and is observed at the early stages of Tau pathology (Zempel & Mandelkow, 2014 ).

Compared to control organoids where Tau normally localizes in axons, SARS-CoV-2-positive neurons exhibited an altered Tau localization pattern, although it was challenging to visualize mislocalization of Tau in 3D tissues. Nevertheless, using selected confocal sections, we could image an altered Tau localization in SARS-CoV-2-positive neurons. In particular, we identified an enhanced level of Tau into the somas of the SARS-CoV-2-positive neurons. Importantly, we could visualize fractions of these neurons still contained Tau and TUJ-1 in their axons, indicating that these neurons are still viable (Fig 2B and C and Appendix Fig S6A).

During the pathogenesis of AD and other Tauopathies, Tau also gets hyperphosphorylated at multiple sites. Sequential phosphorylation at different sites ultimately leads to hyperphosphorylation of Tau (Castellani & Perry, 2019 ). Phosphorylation of Threonine 231 (T231) is one of the first events in the cascade of phosphorylation, and it regulates the microtubule binding. Still, it is also implicated in disease progression such as detachment of Tau from axonal microtubules (Sengupta et al, 1998 Augustinack et al, 2002a , b Luna-Munoz et al, 2007 Alonso et al, 2010 Frost et al, 2015 ). More precisely, we found that compared to control organoids, early Tau phosphorylation marker AT180 recognizes pT231Tau localized at the soma of the SARS-CoV-2-positive neurons (Fig 2D–F). Imaging the neurons for additional phosphorylated Tau using AT8 antibodies (specific for S202 and T205 of Tau) and p396 (specific for S396 of Tau) revealed that unlike pT231Tau, these phospho-species were restricted to the axons and did not mislocalize to the soma of SARS-CoV-2-positive neurons (Appendix Fig S6B–E). In summary, these results demonstrate the aberrant localization of Tau pT231Tau in SARS-CoV-2-positive neurons suggesting the potential neuronal stress reactions upon virus entry.

SARS-CoV-2 induces neuronal cell death

Phosphorylation of Tau at T231 allows for isomerization of the following proline residue into distinct cis- and trans-conformations by the propyl-isomerase PIN1 (Lu et al, 1999 ). Cis-pT231Tau is acutely produced by neurons after traumatic brain injury, leading to disruption of the axonal microtubule network and apoptosis (Nakamura et al, 2012 Kondo et al, 2015 ). Analyzing the nuclei of SARS-CoV-2-positive cells (Fig 3A), we realized that they are highly condensed or fragmented exhibiting a strong reaction to 4′,6-diamidino-2-phenylindole (DAPI) that labels nuclei, a feature quite frequently observed in dead cells. To test neuronal cell death as a consequence of SARS-CoV-2 infection, we stained the SARS-CoV-2-exposed samples with terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) that detects fragmented DNA in dead cells (Darzynkiewicz et al, 2008 ). Compared to un-exposed control organoids, we identified an overall increase in TUNEL-positive cells in SARS-CoV-2-exposed organoids suggesting that virus exposure has caused cell death within 2-dpi (Fig 3B). Staining for SARS-CoV-2-positive cells revealed that most of the virus-positive cells were TUNEL-positive. Besides, we also noticed that some SARS-CoV-2-positive cells were also positive for caspase-3, a protease that specifies programmed cell death (Fig 3C). Interestingly, a fraction of caspase-positive cells displayed pT231Tau localization at the cell soma. Furthermore, TUNEL-positive cells in un-exposed control organoids (which could be after programmed cell death) did not contain pT231Tau suggesting that this different Tau phosphorylation pattern is associated with SARS-CoV-2 entry (Fig 3Ciii). Thus, it appears that Tau is aberrantly phosphorylated in response to the viral-induced stress, which may elicit further cell death programs that remains to be elucidated.

Figure 3. SARS-CoV-2 induces of neuronal death

  • A. Cells from mock organoids display a healthy nucleus labeled by DAPI (blue) (i). SARS-CoV-2-positive cells (green) display condensed (middle panel, ii) and fragmented DNA (bottom panel, iii, arrows). At least 75 cells from two (n = 2) independent batches of organoids were examined. Figures display scale bars.
  • B. Compared to mock organoids, (i) SARS-CoV-2-exposed organoids (ii) display increased TUNEL-positive cells (displayed as inverted LUT) at the cortical plate that is specified by TUJ-1 (magenta). At least four organoids from two (n = 2) independent batches of organoids were examined. Figures display scale bars. The bar diagram below quantifies the frequencies of TUNEL-positive cells between mock and SARS-CoV-2-exposed organoids. Four organoids from two (n = 2) independent batches were examined. Unpaired t-test, *P < 0.05. Data presented as mean ± SD.
  • C. Most of the SARS-CoV-2-positive cells (AB4, green) are TUNEL-positive (i) and some of the SARS-CoV-2-positive cells are caspase-positive (ii). Caspase-positive cells in SARS-CoV-2-exposed organoids display pT231Tau localization at the cell soma specified by AT-180 (ii), which are not observed in mock organoids (iii). Arrowheads point SARS-CoV-2-positive cells (AB4, green) that are also positive for TUNNEL (red), caspase 3 (yellow), and Tau AT-180 (magenta). Figures display scale bars. Bar diagrams at right quantifies proportions of TUNEL and caspase-positive cells among SARS-CoV-2-positive cells. The second graph below quantifies the proportions of pT231Tau-positive cells among caspase-positive cells between control and virus exposed groups. At least 400 cells from four organoids and two independent (n = 2) batches were examined. Unpaired t-test, **P < 0.01. Data presented as mean ± SEM. TUNEL-positive cells in control un-exposed organoids do not contain pT231Tau (iii). Figures display scale bars. Bar diagrams at right quantifies proportions of pT231Tau-positive cells among TUNNEL-positive cells between control and virus exposed groups. At least 350 cells from 4 organoids and two independent (n = 2) batches were examined. Unpaired t-test, ***P < 0.001. Data presented as mean ± SEM.

From our June 2016 issue

Check out the full table of contents and find your next story to read.

Galton launched a debate that raged throughout the 20th century over nature versus nurture. Are our actions the unfolding effect of our genetics? Or the outcome of what has been imprinted on us by the environment? Impressive evidence accumulated for the importance of each factor. Whether scientists supported one, the other, or a mix of both, they increasingly assumed that our deeds must be determined by something.

In recent decades, research on the inner workings of the brain has helped to resolve the nature-nurture debate—and has dealt a further blow to the idea of free will. Brain scanners have enabled us to peer inside a living person’s skull, revealing intricate networks of neurons and allowing scientists to reach broad agreement that these networks are shaped by both genes and environment. But there is also agreement in the scientific community that the firing of neurons determines not just some or most but all of our thoughts, hopes, memories, and dreams.

We know that changes to brain chemistry can alter behavior—otherwise neither alcohol nor antipsychotics would have their desired effects. The same holds true for brain structure: Cases of ordinary adults becoming murderers or pedophiles after developing a brain tumor demonstrate how dependent we are on the physical properties of our gray stuff.

Many scientists say that the American physiologist Benjamin Libet demonstrated in the 1980s that we have no free will. It was already known that electrical activity builds up in a person’s brain before she, for example, moves her hand Libet showed that this buildup occurs before the person consciously makes a decision to move. The conscious experience of deciding to act, which we usually associate with free will, appears to be an add-on, a post hoc reconstruction of events that occurs after the brain has already set the act in motion.

The 20th-century nature-nurture debate prepared us to think of ourselves as shaped by influences beyond our control. But it left some room, at least in the popular imagination, for the possibility that we could overcome our circumstances or our genes to become the author of our own destiny. The challenge posed by neuroscience is more radical: It describes the brain as a physical system like any other, and suggests that we no more will it to operate in a particular way than we will our heart to beat. The contemporary scientific image of human behavior is one of neurons firing, causing other neurons to fire, causing our thoughts and deeds, in an unbroken chain that stretches back to our birth and beyond. In principle, we are therefore completely predictable. If we could understand any individual’s brain architecture and chemistry well enough, we could, in theory, predict that individual’s response to any given stimulus with 100 percent accuracy.

This research and its implications are not new. What is new, though, is the spread of free-will skepticism beyond the laboratories and into the mainstream. The number of court cases, for example, that use evidence from neuroscience has more than doubled in the past decade—mostly in the context of defendants arguing that their brain made them do it. And many people are absorbing this message in other contexts, too, at least judging by the number of books and articles purporting to explain “your brain on” everything from music to magic. Determinism, to one degree or another, is gaining popular currency. The skeptics are in ascendance.

This development raises uncomfortable—and increasingly nontheoretical—questions: If moral responsibility depends on faith in our own agency, then as belief in determinism spreads, will we become morally irresponsible? And if we increasingly see belief in free will as a delusion, what will happen to all those institutions that are based on it?

In 2002, two psychologists had a simple but brilliant idea: Instead of speculating about what might happen if people lost belief in their capacity to choose, they could run an experiment to find out. Kathleen Vohs, then at the University of Utah, and Jonathan Schooler, of the University of Pittsburgh, asked one group of participants to read a passage arguing that free will was an illusion, and another group to read a passage that was neutral on the topic. Then they subjected the members of each group to a variety of temptations and observed their behavior. Would differences in abstract philosophical beliefs influence people’s decisions?

Yes, indeed. When asked to take a math test, with cheating made easy, the group primed to see free will as illusory proved more likely to take an illicit peek at the answers. When given an opportunity to steal—to take more money than they were due from an envelope of $1 coins—those whose belief in free will had been undermined pilfered more. On a range of measures, Vohs told me, she and Schooler found that “people who are induced to believe less in free will are more likely to behave immorally.”

It seems that when people stop believing they are free agents, they stop seeing themselves as blameworthy for their actions. Consequently, they act less responsibly and give in to their baser instincts. Vohs emphasized that this result is not limited to the contrived conditions of a lab experiment. “You see the same effects with people who naturally believe more or less in free will,” she said.

In another study, for instance, Vohs and colleagues measured the extent to which a group of day laborers believed in free will, then examined their performance on the job by looking at their supervisor’s ratings. Those who believed more strongly that they were in control of their own actions showed up on time for work more frequently and were rated by supervisors as more capable. In fact, belief in free will turned out to be a better predictor of job performance than established measures such as self-professed work ethic.

Another pioneer of research into the psychology of free will, Roy Baumeister of Florida State University, has extended these findings. For example, he and colleagues found that students with a weaker belief in free will were less likely to volunteer their time to help a classmate than were those whose belief in free will was stronger. Likewise, those primed to hold a deterministic view by reading statements like “Science has demonstrated that free will is an illusion” were less likely to give money to a homeless person or lend someone a cellphone.

Further studies by Baumeister and colleagues have linked a diminished belief in free will to stress, unhappiness, and a lesser commitment to relationships. They found that when subjects were induced to believe that “all human actions follow from prior events and ultimately can be understood in terms of the movement of molecules,” those subjects came away with a lower sense of life’s meaningfulness. Early this year, other researchers published a study showing that a weaker belief in free will correlates with poor academic performance.

The list goes on: Believing that free will is an illusion has been shown to make people less creative, more likely to conform, less willing to learn from their mistakes, and less grateful toward one another. In every regard, it seems, when we embrace determinism, we indulge our dark side.

Few scholars are comfortable suggesting that people ought to believe an outright lie. Advocating the perpetuation of untruths would breach their integrity and violate a principle that philosophers have long held dear: the Platonic hope that the true and the good go hand in hand. Saul Smilansky, a philosophy professor at the University of Haifa, in Israel, has wrestled with this dilemma throughout his career and come to a painful conclusion: “We cannot afford for people to internalize the truth” about free will.

Smilansky is convinced that free will does not exist in the traditional sense—and that it would be very bad if most people realized this. “Imagine,” he told me, “that I’m deliberating whether to do my duty, such as to parachute into enemy territory, or something more mundane like to risk my job by reporting on some wrongdoing. If everyone accepts that there is no free will, then I’ll know that people will say, ‘Whatever he did, he had no choice—we can’t blame him.’ So I know I’m not going to be condemned for taking the selfish option.” This, he believes, is very dangerous for society, and “the more people accept the determinist picture, the worse things will get.”

Determinism not only undermines blame, Smilansky argues it also undermines praise. Imagine I do risk my life by jumping into enemy territory to perform a daring mission. Afterward, people will say that I had no choice, that my feats were merely, in Smilansky’s phrase, “an unfolding of the given,” and therefore hardly praiseworthy. And just as undermining blame would remove an obstacle to acting wickedly, so undermining praise would remove an incentive to do good. Our heroes would seem less inspiring, he argues, our achievements less noteworthy, and soon we would sink into decadence and despondency.

Smilansky advocates a view he calls illusionism—the belief that free will is indeed an illusion, but one that society must defend. The idea of determinism, and the facts supporting it, must be kept confined within the ivory tower. Only the initiated, behind those walls, should dare to, as he put it to me, “look the dark truth in the face.” Smilansky says he realizes that there is something drastic, even terrible, about this idea—but if the choice is between the true and the good, then for the sake of society, the true must go.

Smilansky’s arguments may sound odd at first, given his contention that the world is devoid of free will: If we are not really deciding anything, who cares what information is let loose? But new information, of course, is a sensory input like any other it can change our behavior, even if we are not the conscious agents of that change. In the language of cause and effect, a belief in free will may not inspire us to make the best of ourselves, but it does stimulate us to do so.

Illusionism is a minority position among academic philosophers, most of whom still hope that the good and the true can be reconciled. But it represents an ancient strand of thought among intellectual elites. Nietzsche called free will “a theologians’ artifice” that permits us to “judge and punish.” And many thinkers have believed, as Smilansky does, that institutions of judgment and punishment are necessary if we are to avoid a fall into barbarism.

Smilansky is not advocating policies of Orwellian thought control. Luckily, he argues, we don’t need them. Belief in free will comes naturally to us. Scientists and commentators merely need to exercise some self-restraint, instead of gleefully disabusing people of the illusions that undergird all they hold dear. Most scientists “don’t realize what effect these ideas can have,” Smilansky told me. “Promoting determinism is complacent and dangerous.”

Yet not all scholars who argue publicly against free will are blind to the social and psychological consequences. Some simply don’t agree that these consequences might include the collapse of civilization. One of the most prominent is the neuroscientist and writer Sam Harris, who, in his 2012 book, Free Will, set out to bring down the fantasy of conscious choice. Like Smilansky, he believes that there is no such thing as free will. But Harris thinks we are better off without the whole notion of it.

“We need our beliefs to track what is true,” Harris told me. Illusions, no matter how well intentioned, will always hold us back. For example, we currently use the threat of imprisonment as a crude tool to persuade people not to do bad things. But if we instead accept that “human behavior arises from neurophysiology,” he argued, then we can better understand what is really causing people to do bad things despite this threat of punishment—and how to stop them. “We need,” Harris told me, “to know what are the levers we can pull as a society to encourage people to be the best version of themselves they can be.”

According to Harris, we should acknowledge that even the worst criminals—murderous psychopaths, for example—are in a sense unlucky. “They didn’t pick their genes. They didn’t pick their parents. They didn’t make their brains, yet their brains are the source of their intentions and actions.” In a deep sense, their crimes are not their fault. Recognizing this, we can dispassionately consider how to manage offenders in order to rehabilitate them, protect society, and reduce future offending. Harris thinks that, in time, “it might be possible to cure something like psychopathy,” but only if we accept that the brain, and not some airy-fairy free will, is the source of the deviancy.

Accepting this would also free us from hatred. Holding people responsible for their actions might sound like a keystone of civilized life, but we pay a high price for it: Blaming people makes us angry and vengeful, and that clouds our judgment.

“Compare the response to Hurricane Katrina,” Harris suggested, with “the response to the 9/11 act of terrorism.” For many Americans, the men who hijacked those planes are the embodiment of criminals who freely choose to do evil. But if we give up our notion of free will, then their behavior must be viewed like any other natural phenomenon—and this, Harris believes, would make us much more rational in our response.

Although the scale of the two catastrophes was similar, the reactions were wildly different. Nobody was striving to exact revenge on tropical storms or declare a War on Weather, so responses to Katrina could simply focus on rebuilding and preventing future disasters. The response to 9/11, Harris argues, was clouded by outrage and the desire for vengeance, and has led to the unnecessary loss of countless more lives. Harris is not saying that we shouldn’t have reacted at all to 9/11, only that a coolheaded response would have looked very different and likely been much less wasteful. “Hatred is toxic,” he told me, “and can destabilize individual lives and whole societies. Losing belief in free will undercuts the rationale for ever hating anyone.”

Whereas the evidence from Kathleen Vohs and her colleagues suggests that social problems may arise from seeing our own actions as determined by forces beyond our control—weakening our morals, our motivation, and our sense of the meaningfulness of life—Harris thinks that social benefits will result from seeing other people’s behavior in the very same light. From that vantage point, the moral implications of determinism look very different, and quite a lot better.

What’s more, Harris argues, as ordinary people come to better understand how their brains work, many of the problems documented by Vohs and others will dissipate. Determinism, he writes in his book, does not mean “that conscious awareness and deliberative thinking serve no purpose.” Certain kinds of action require us to become conscious of a choice—to weigh arguments and appraise evidence. True, if we were put in exactly the same situation again, then 100 times out of 100 we would make the same decision, “just like rewinding a movie and playing it again.” But the act of deliberation—the wrestling with facts and emotions that we feel is essential to our nature—is nonetheless real.

The big problem, in Harris’s view, is that people often confuse determinism with fatalism. Determinism is the belief that our decisions are part of an unbreakable chain of cause and effect. Fatalism, on the other hand, is the belief that our decisions don’t really matter, because whatever is destined to happen will happen—like Oedipus’s marriage to his mother, despite his efforts to avoid that fate.

When people hear there is no free will, they wrongly become fatalistic they think their efforts will make no difference. But this is a mistake. People are not moving toward an inevitable destiny given a different stimulus (like a different idea about free will), they will behave differently and so have different lives. If people better understood these fine distinctions, Harris believes, the consequences of losing faith in free will would be much less negative than Vohs’s and Baumeister’s experiments suggest.

Can one go further still? Is there a way forward that preserves both the inspiring power of belief in free will and the compassionate understanding that comes with determinism?

Philosophers and theologians are used to talking about free will as if it is either on or off as if our consciousness floats, like a ghost, entirely above the causal chain, or as if we roll through life like a rock down a hill. But there might be another way of looking at human agency.

Some scholars argue that we should think about freedom of choice in terms of our very real and sophisticated abilities to map out multiple potential responses to a particular situation. One of these is Bruce Waller, a philosophy professor at Youngstown State University. In his new book, Restorative Free Will, he writes that we should focus on our ability, in any given setting, to generate a wide range of options for ourselves, and to decide among them without external constraint.

For Waller, it simply doesn’t matter that these processes are underpinned by a causal chain of firing neurons. In his view, free will and determinism are not the opposites they are often taken to be they simply describe our behavior at different levels.

Waller believes his account fits with a scientific understanding of how we evolved: Foraging animals—humans, but also mice, or bears, or crows—need to be able to generate options for themselves and make decisions in a complex and changing environment. Humans, with our massive brains, are much better at thinking up and weighing options than other animals are. Our range of options is much wider, and we are, in a meaningful way, freer as a result.

Waller’s definition of free will is in keeping with how a lot of ordinary people see it. One 2010 study found that people mostly thought of free will in terms of following their desires, free of coercion (such as someone holding a gun to your head). As long as we continue to believe in this kind of practical free will, that should be enough to preserve the sorts of ideals and ethical standards examined by Vohs and Baumeister.

Yet Waller’s account of free will still leads to a very different view of justice and responsibility than most people hold today. No one has caused himself: No one chose his genes or the environment into which he was born. Therefore no one bears ultimate responsibility for who he is and what he does. Waller told me he supported the sentiment of Barack Obama’s 2012 “You didn’t build that” speech, in which the president called attention to the external factors that help bring about success. He was also not surprised that it drew such a sharp reaction from those who want to believe that they were the sole architects of their achievements. But he argues that we must accept that life outcomes are determined by disparities in nature and nurture, “so we can take practical measures to remedy misfortune and help everyone to fulfill their potential.”

Read Follow-Up Notes

Understanding how will be the work of decades, as we slowly unravel the nature of our own minds. In many areas, that work will likely yield more compassion: offering more (and more precise) help to those who find themselves in a bad place. And when the threat of punishment is necessary as a deterrent, it will in many cases be balanced with efforts to strengthen, rather than undermine, the capacities for autonomy that are essential for anyone to lead a decent life. The kind of will that leads to success—seeing positive options for oneself, making good decisions and sticking to them—can be cultivated, and those at the bottom of society are most in need of that cultivation.

To some people, this may sound like a gratuitous attempt to have one’s cake and eat it too. And in a way it is. It is an attempt to retain the best parts of the free-will belief system while ditching the worst. President Obama—who has both defended “a faith in free will” and argued that we are not the sole architects of our fortune—has had to learn what a fine line this is to tread. Yet it might be what we need to rescue the American dream—and indeed, many of our ideas about civilization, the world over—in the scientific age.


Current explanations from the field of neuroscience suggest that déjà vu occurs when the brain is slightly fatigued and working to 'fact check' a memory. We experience this as being odd because we become aware of the process.

Might we explore a different explanation for déjà vu if we were looking at it from the standpoint of time being non linear and perhaps opening up to the idea of a collective consciousness?

Take a moment and breathe. Place your hand over your chest area, near your heart. Breathe slowly into the area for about a minute, focusing on a sense of ease entering your mind and body. Click here to learn why we suggest this.

They say about 60% of people experience déjà vu during their life, right off the bat that hit me as something I didn’t expect as I feel like almost everyone I know has had it at one time or another. Déjà vu, (‘already seen’ to the French) is the feeling that you are re-living something that has happened before. In the movie The Matrix, where déjà vu is perhaps most thought of in pop culture, Neo experiences a cat going by a doorway twice in a matter of seconds. Same cat, same moves, same everything.

In the film, this moment is presented as a ‘glitch in the matrix,’ however, in real life, déjà vu doesn’t often happen like what is seen in The Matrix, it instead feels as though you can’t recall when the ‘other memory’ happened, more so that what you are experiencing right now has already happened at some time.

Let’s dive into what some believe neuroscience is offering as an explanation.

What Happened:

According to experts like Dr Akira O’Connor, who is a senior psychology lecturer at the University of St Andrews, déjà vu is not only a feeling of familiarity, but also the metacognitive recognition that these feelings are misplaced. In simple terms:

“Déjà vu is basically a conflict between the sensation of familiarity and the awareness that the familiarity is incorrect. And it’s the awareness that you’re being tricked that makes déjà vu so unique compared to other memory events.”

Neuroscientists have determined that this memory illusion occurs when the frontal regions of the brain are attempting to correct an inaccurate memory.

“For the vast majority of people, experiencing déjà vu is probably a good thing. It’s a sign that the fact-checking brain regions are working well, preventing you from misremembering events. In a healthy person, such misremembering is going to happen every day. This is to be expected because your memory involves millions and billions of neurones. It’s very messy.”

While there isn’t a completely agreed upon explanation for what happens in the brain when déjà vu occurs, most models suggest that déjà vu occurs when areas of the brain (such as the temporal lobe) feed the mind’s frontal regions signals that a past experience is repeating itself. The frontal decision making parts of the brain then checks to see if the memory is actually true or possible, perhaps saying something to the effect “have I been here before?”

“If you have actually been in that place before, you may try harder to retrieve more memories. If not, a déjà vu realization can occur.”

It’s typically believed that we are more susceptible to déjà vu when the mind is a bit more fatigued and not as quick to discern that validity of our current moment.

Why It Matters:

What fascinated me about this in particular is two things: I’ve long felt that it’s quite possible that memories may actually be non local, i.e. they exist outside the brain not in the brain, and that perhaps the brain tunes into those memories that are somewhere around us. Or maybe we could say that some memory may exist in the brain, while others are part of some sort of collective field.

The second fascinating part for me is that I wonder if déjà vu has something to do with emerging science that tells us time is not linear. Perhaps when we take a classic scientific model that states all time is linear and all experience is linear, we limit our explanation of what déjà vu might be to something that fits that paradigm. What if the brain is tuning into something relating to quantum potentials that always exist, and that perhaps something different is happening with déjà vu? I’m not sure yet, however this is where déjà vu intrigues me the most.

Of course, the end result of exploring a question like this invites us to shift our worldview around the nature of reality, time and experience. Something that might be uncomfortable for some but I feel post material science is inviting us to do.

The Takeaway:

As with anything that is happening in our lives right now it seems, we are culturally in a time where a long avoided shift in our scientific paradigm is creating a lack of meaningful explanations for many things that happen in life. Is déjà vu one of those things that doesn’t have a good explanation in our current scientific paradigm? The jury might still be out on that, but for me, the current explanation presented in this piece did not quite ‘do it for me’ and my inquisitive mind and gut feeling pushes me to explore these questions through the emerging paradigm of non material science.

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The science of smell

Us humans get a lot from our sense of smell. But other animals, such as dogs, rely on their noses so much more. Why might that be? And why did we evolve to have the noses we do? New research in Science Advances sheds some light on those questions and Darren Logan from the Waltham Centre for Pet Nutrition joined Katie Haylor in the studio to chat all about it.

Darren - Our sense of smell is essentially a chemical scent. So the volatiles that's coming off the freshly baked bread - into the atmosphere - is what we're sensing through our sense of smell. And every time you take a deep breath in, the volatiles will rush through your nose and hit these molecular receptors on the surface of your nose called olfactory receptors and they exist in olfactory sensory neurons.

In a human nose we have about 300 different types of these neurons and each one detects a different combination of small molecules and it's the combination and the pattern of those together that your brain interprets as the smell of - in this case freshly baked bread.

Katie - 300 doesn't sound like an awful lot so how do we end up with the incredible amount of smells that we're able to recognize?

Darren - So that is the sort of the real trick of your olfactory system. So it's due to something called combinatorial coding, so each receptor - each of the 300 receptors - can detect a combination of different molecules and each molecule can activate a combination of different receptors. So when you multiply those together we think that we can detect up to a trillion different orders.

Katie - Okay. So how did you analyse smell in this study? What were you interested in?

Darren - So we took advantage of a particular quirk of the olfactory system - which is that each sensory neuron in the nose expresses just one olfactory receptor. And so because we knew that, we were able to use something called RNA sequencing to quantify the RNA of each receptor and that allows us to tell us the number of each type of neuron the nose. You might expect that of the 300 neural types, they'd all be equally represented. And what we found out - that wasn't the case. There were a very small number, actually, about 10 or 15 are very very highly represented in the nose and the vast majority are relatively lowly represented.

Katie - So what does this mean then? What do we take away from this?

Darren - So we were really interested about what these receptors are - there are these neurons that are there in very high abundance. And so, what we did is, we looked at what those neurons are detecting and what we found in the case of humans is that they're detecting what we call key food orders. So these are the orders that are produced by our food. So - as you mentioned - the orders in freshly baked bread.

When we looked in other species - we looked in mice, rats, dogs and a number of primates - we found that wasn't the case. And likewise, when we looked in mice, we found that the neurons that were very abundant actually detect pheromones - so cues that the mice used to sexual communicate with each other. So what we think that means is that each mammal has evolved to to have a nose that is very specific to its niche.

Katie - Can we take from that that sourcing food is particularly important to us but there may be other equally pressing matters for other animals. How would you pick that apart?

Darren - Yeah that's our hypothesis. There was a theory that our senses of smells were essentially not under evolutionary pressure. They're just drifting around and they can detect anything that we happen to run into. What this research - we think - suggests that's not true, that actually our noses are tuned and over time have been tuned to the things that are important to us - to promote our reproduction and our survival. In the case of humans, our sense of smell is particularly important for detecting food and scavenging for food. And that's why we think our noses are tuned the way they are.

Katie - So more receptors equals better smelling ability. Is that pretty much right?

Darren - This is a bit of a mystery in the olfactory field. Species like dogs or mice or indeed elephants, who we think have the most receptors, may be able to smell more, but we actually think at the moment that it's likely that they don't smell more - they just discriminate better. So they can tell subtle differences between things that we as humans - who are not the best smellers in the world - probably couldn't.

Katie - Having said we're not the best smellers in the world, I've got to say, I’ve named myself the bloodhound of the Naked Scientist office because I feel like my sense of smell is really good. Why would that be? Why would I be better at smelling than say Izzie, for instance?

Darren - Well there are people who are better at smelling than others and it probably down to genetic variation. We know there is a lot of variation in the olfactory receptors, however, we also know that people who are often deemed or described as better smellers are often more verbal - so are able to describe the scent, the smells that they detect better and that appears that they’re therefore better but they're actually better explaining it.

Katie - I'm still going to take credit for that one. Very briefly. what's the next step then with this particular piece of work?

Darren - We are doing two things - I guess - one is that we are looking to spatially identify the position of the neurons in the nose, rather than just the abundance of them. And this is important because when you smell, the air rushes through the nose and depending on which parts of the nose it hits, we might think it works differently. And secondly, we are particularly interested in those abundant neurons and finding out exactly what they detect.


Building Blocks of the Nervous System

The building block of the nervous system is the neuron. (Fun fact: The human brain contains approximately 100 billion neurons. That’s more than 14x the number of human beings currently on planet Earth!)

The anatomy of a neuron may differ slightly based on its function but the structures that comprise it remain the same.

Another cell type central to the functioning of the nervous system (pun intended) is the glial cell. Glial cells

  1. Help support and hold neurons in place
  2. Protect neurons
  3. Create myelin which helps to move nerve impulses
  4. Repair neurons and help restore neuron function
  5. Trim out dead neurons
  6. Regulate neurotransmitters

Neuron Anatomy

The neuron is composed of the following parts (NICHD, 2018):

  • Nucleus
  • Cell body
  • Dendrite: responsible for receiving information via synapses for the cell to process and send through the axon, through the axon terminal, to the synapse to be passed on again
  • Axon: the “corridor” through which sensory information is passed to the dendrite to be sent to another neuron. (The term “nerve” doesn’t refer to a neuron, even though it seems like it should. What it actually refers to is a collection of multiple axons is a collection of axons that work together as a collective. In the same way that a grouping of asparagus stalks makes one bunch of asparagus, a bundle of axons makes one nerve. The different types of nerves are cervical, thoracic, lumbar and sacral nerves.)
  • Myelin sheath: a fatty tissue that insulates the axon by preventing depolarization. This allows electrical impulses to travel across the axon uninterrupted.
  • Node of Ranvier: these are the gaps in the myelin sheath. Their function is to speed up propagation of action potentials along the axon via saltatory conduction. (Because these gaps are not myelinated, the action potentials appear to jump between nodes like the water fountains at Disneyworld.) Saltatory conduction also helps to conserve energy by decreasing the required movement of ions by 100x.
  • Axon terminal: the end of the axon, the last stop before electrical impulses are sent through the synapse. This structure converts electrical impulses into chemical signals which, when released, are then called neurotransmitters. Neurotransmitters pass through the synapse to the next dendrite and are then converted back into an electrical impulse to repeat this process until it reaches the proper organ.

Types of Neurons

There are four different types of neurons. Their anatomy is determined by what types of information they’ll need to propagate, to what organs, and in what region of the body.

In fact, there are so many different types of neurons in the brain alone that they are not yet all described. This is because neurons in the brain will differ based on what part of the target neuron they’re communicating to (dendrite vs. axon), express different genes, express varying electrical impulses, and several more distinctions.

The types of neurons are below:

Neurons in the spinal cord are sensory and motor neurons. Sensory neurons are activated by sensory input from the environment (touch, taste, smell, sound, sight). The input of information from the external environment can be physical or chemical and correspond to all five senses. Most sensory neurons are pseudounipolar.

Motor neurons in the spinal cord are a part of CNS and connect to muscles, glands, and organs throughout the body. They transmit impulses from the spinal cord to skeletal and smooth muscle tissues based on information gathered by the sensory neurons. These are typically multipolar.

The lower motor neurons extend from the spinal cord to the muscles and the upper motor neurons travel from the brain and spinal cord to the distal parts of the body.

Lastly, there are interneurons. These serve as the connections between spinal, motor, and sensory neurons and communicate with each other by forming networks throughout the body, the structure of which differs based on need and organ system. These neurons are multipolar as well.


Whatever you think though, whether you think he has proved his case or not, nobody, surely, has yet proved the opposite position - that all physics including human behaviour, can be simulated in a computer.

I'm not sure why so many are so sure of that conclusion - that a computer program can simulate a human - when nobody has proved it or has any idea yet of how to prove it.

And - it seems at least to be a reasonable philosophical position for anyone to take, that the human mind is essentially non computable.

Myself anyway - I now expect that some time or another down the road, we will hit some truly non computable physics. Though whether in the form of Roger Penrose's Orch Or theory or some other idea - maybe not involving quantum mechanics at all - or even quantum gravity - I don't know.

It's interesting as an area of philosophy which leads to an actual prediction about the future of science. A clear prediction that computer programs will never fully emulate human beings, so long as they are based on physics as currently understood, or computer programs as we currently understand those.

And then based on Roger Penrose's ideas - I think it is reasonable also to say that it predicts non computable areas of physics.

Is there any other area of pure philosophy that leads to a future science prediction?

And that then leads to the idea that if we ever do have Artificial Intelligence then it is likely to be impossible to program, in some intrinsic way because at its core, it is in some way doing something essentially non computable. And, I think myself, that means that it would start off like a young thing or baby - or may be an "uplifted species" or similar.

I'm Robert Walker, inventor & programmer. I have had a long term special interest in astronomy, and space science since the 1970s, and most of.


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