cover image credit: Ryo Minemizu (for more, see https://www.ryo-minemizu.com/)

Anglerfish https://en.wikipedia.org/wiki/Anglerfish

Notes: males are tiny compared to females. In many species of anglerfish, mating occurs through males attaching to and then fusing with females such that their circulatory systems join together. The male provides sperm and is eventually absorbed into the female.

Image: [credit: Dante Fenolio]

Basking shark https://en.wikipedia.org/wiki/Basking_shark

Notes: second largest type of shark (after the whale shark), reaches about 7.9 m in length. They feed on plankton by swimming forwards with the mouth open and filtering via gill rakers.

Image: [credit: Wikipedia page]

Blanket octopus https://en.wikipedia.org/wiki/Blanket_octopus

Notes: characterized by transparent flaps connecting the dorsal and dorsolateral tentacles in adult females. In a case of extreme sexual dimorphism, the females grow to around 2 m in size while the males are only about 2.4 cm.

Image: [credit: Steve Hamedl]

Colossal squid https://en.wikipedia.org/wiki/Colossal_squid

Notes: at around 10 m long, they are shorter than giant squid, but they are much heavier. Colossal squid also has the largest eyes of any organism (around 30-40 cm in diameter).

Image: [credit: NPR]

Crinoid https://en.wikipedia.org/wiki/Crinoid

Notes: depending on the type crinoid adults can either swim freely or be tethered to the sea floor by a stalk. The former are called feather stars and the latter are called sea lillies. Crinoids can also crawl using rootlike structures called cirri as legs. They consume plankton and detritus by filtering through their featherlike arms and then propelling it towards a mouth. They reproduce sexually, releasing sperm and eggs into the water. Fertilized eggs hatch into freely swimming larvae that settle on the sea floor and transition into a stalked juvenile state before eventually breaking away (in the case of feather stars) as adults to swim freely once more.

Image: [credit: Wikipedia page]

Cuttlefish https://en.wikipedia.org/wiki/Cuttlefish

Notes: cuttlefish are among the most intelligent invertebrates. They can rapidly change color using their chromatophore cells as a mode of communication and camouflage as well as to warn off predators (called a deimatic display).

Image: [credit: Wikipedia page]

Garden eels https://en.wikipedia.org/wiki/Heterocongrinae

Notes: garden eels are distinguished by their behavior of living in burrows on the sea floor and poking their heads out of the burrows to eat prey and sliding back into the burrows to avoid predators. Colonies of them can resemble grasses, hence their name.

Image: [credit: Insider]

Glaucus atlanticus https://en.wikipedia.org/wiki/Glaucus_atlanticus

Notes: a type of small (up to 3 cm) sea slug that floats upside down using a gas-filled stomach to stay adrift. They feed on Portuguese man o’ war jellyfish and similar organisms. After consuming venomous nematocysts from their prey, they store the stinging cells in sacs (called cnidosacs) located in their own extremities. This concentrates the nematocysts, making the sting of Glaucus atlanticus potentially more potent even than that of the man o’ war jellyfish itself.

Image: [credit: Wikipedia page]

Hawaiian bobtail squid https://en.wikipedia.org/wiki/Euprymna_scolopes

Notes: enjoys a symbiotic relationship with bioluminescent Aliivibrio fischeri bacteria that inhabit a light organ in the squid’s mantle.

Image: [credit: Wikipedia page]

Japanese spider crab https://en.wikipedia.org/wiki/Japanese_spider_crab

Notes: has the largest legspan of any arthropod, reaching about 3.7 m across.

Image: [credit: Kids Discover]

Lion’s mane jellyfish https://en.wikipedia.org/wiki/Lion’s_mane_jellyfish

Notes: one of the largest types of jellyfish. Though their sizes vary widely, some can reach a bell diameters of more than 2 m and possess tentacles extending for over 30 m in length.

Image: [credit: The Toronto Star]

Placozoa https://en.wikipedia.org/wiki/Placozoa

Notes: a category of animals that exist as flat sheets a few cells thick with a ciliated epithelium on their undersides. They use these cilia to move along the seafloor and most of them reproduce asexually by budding or fragmenting into smaller individuals (though one subtype does also reproduce sexually).

Image: [credit: Wikipedia page]

Prochlorococcus https://en.wikipedia.org/wiki/Prochlorococcus

Notes: a type of marine cyanobacteria which represents perhaps the most abundant photosynthetic organism on Earth.

Image: [credit: Wikipedia page]

Sunflower sea star https://en.wikipedia.org/wiki/Sunflower_sea_star

Notes: large sea stars which can reach diameters of 1 m. Sunflower sea stars are predatory and consume various prey such as sea urchins, other sea stars, clams, sea cucumbers, and more. They move at a speed of about a meter per minute using thousands of tube feet located on their undersides.

Image: [credit: Wikipedia page]

Vampire squid https://en.wikipedia.org/wiki/Vampire_squid

Notes: a deep sea cephalopod of about 30 cm length that lives in the ocean’s aphotic zone. Vampire squid (Vampyroteuthis infernalis) have flaps of tissue connecting their tentacles, each of which is lined by fleshy spines. It is covered in photophores that produce disorientating flashes of light to confuse predators. Rather than ink, they can eject a sticky cloud of bioluminescent mucus when highly agitated by predators.

Image: [credit: Wikipedia page]

Humongous Fungus, a specimen of Armillaria ostoyae, has claimed the title of world’s largest single organism. Though it features honey mushrooms above ground, the bulk of this creature’s mass arises from its vast subterranean mycelial network of filamentous tendrils. It has spread across more than 2,000 acres of soil and weighs over 30,000 metric tons. Yet I would contend that Humongous Fungus represents a mere microcosm of the world’s true largest organism, a creature that I will call Cyborg Earth. What is Cyborg Earth? Eastern religions have suggested that all life is fundamentally interconnected. Cyborg Earth represents an extension of this concept.

All across the globe, biological life thrives. Quintillions upon quintillions of biomolecular computations happen every second, powering all life. Mycoplasma bacteria. Communities of leafcutter ants. The Humongous Fungus. Beloved beagles. Seasonal influenza viruses. Parasitic roundworms. Families of Canadian elk. Vast blooms of cyanobacteria. Humanity. Life works because of complexity that arises from simplicity that in turn arises from whatever inscrutable quantum mechanical rules lay beneath the molecular scale.

All creatures rearrange atoms in various ways. Termites and beavers rearrange larger bunches of atoms than most organisms. As humans progressed from paleolithic to metalwork to industrialization and then to the space age, information revolution, and era of artificial intelligence, they learned to converse with the atoms around them in an ever more complex fashion. We are actors in an operatic performance, we are subroutines of evolution, we are interwoven matryoshka patterns, an epic chemistry.

Thermodynamics and memetic natural selection juggle our civilization while riding a unicycle along a path to an as-yet unknown destiny. Because of humanity, technology represents a fundamental part of the biosphere. Computers, airplanes, factories, shipping routes. All of it is natural. This does not mean it is good or bad. Simply that it comprises part of the great biological conversation. This is Cyborg Earth. The world’s largest organism is the biosphere itself, including all of the remarkable ways that its constituents have reshaped its body.

Cyborg Earth has always been a uniquely gleaming gem in the cold vastness of the cosmos. But I believe that Cyborg Earth still resides in an embryonic state. As we hurtle into the expanse of the infinite future, Cyborg Earth will grow and change. We must realize that we are all connected by the electric dance of atoms. As a constituent species within Cyborg Earth, we currently possess an enormous power and a tremendous responsibility to properly steward our world’s embryogenesis.

Cover image modified from “Confocal 3D-image of a fungal network with reproductive spores containing nuclei” by Vasilis Kokkoris, source.

By Logan Thrasher Collins

Click here for PDF version

Combining synchrotron x-ray microscopy with expansion microscopy may represent a feasible approach for whole human brain connectomics at the nanoscale. Synchrotron x-ray microtomography on its own provides extremely fast imaging at high resolution, yet necessary tradeoffs between imaging throughput and resolution mean that the imaging an entire human brain with voxel sizes of less than 100 nm may still take much too long with current synchrotron technology. Fast imaging with voxel sizes of 300-1000 nm is much more readily achievable in the near future. Furthermore, expansion microscopy isotropically enlarges tissue by infusion of a swellable hydrogel, facilitating resolution increases. The combination of x-ray microtomography and expansion microscopy (hereafter referred to as ExxRM) could thus push the effective voxel size down to the level needed for dense connectomics. However, because tissue volume and imaging time scale cubically with expansion factor, careful balance between design of the synchrotron x-ray optical setup and the degree of expansion will be vital. In this perspective, I will explore balances between synchrotron optical engineering choices and expansion factor, propose methods to successfully implement ExxRM in the context of human brains, and estimate how much it would cost to image the human brain in this way. Imaging brains via ExxRM may represent a crucial paradigm shift in connectomics which paves the way for holistic understanding of human brain function.

Introduction

Nanoscale connectomic imaging of the entire human brain represents a long sought-after goal that could provide the foundation for dramatic advances in neurobiology, neurotechnology, and artificial intelligence.^1,2 Currently, the leading method for nanoscale connectomics is volume electron microscopy (EM). But imaging a 1 mm³ volume of mouse cortex over a period of 6 months required a tremendous collaborative effort by Yin et al. to develop a parallelized and fully automated transmission electron microscopy (TEM) system consisting of six instruments working in parallel.³ Each of these six instruments cost $125,000. The mouse brain has a volume of roughly 500 mm³, meaning that if these numbers were directly scaled, the process would take 250 years.⁴ That said, Yin et al.’s TEM dataset had high resolution with 4×4×40 nm voxels, so throughput might be increased by imaging at somewhat lower resolution. As such, it is conceivable to argue that advances in EM technology may enable imaging of an entire mouse brain at sub-100 nm voxel size over the course of a few years at a cost of around $10M. EM therefore represents a viable option for mouse brain connectomics. But the human brain’s volume is roughly 1200 cm³, around 2400-fold larger than the mouse brain.⁵ Even if EM technology somehow advances to the point where an entire mouse brain can be imaged at 100 nm³ voxel size in a single year for $10M, mapping a human brain with comparable parameters would take thousands of years. In my view, this provides a strong argument for the idea that a radically different approach is necessary for human brain connectomics.

Expansion light-sheet fluorescence microscopy represents a promising alternative to EM, yet this modality also falls short when considering the volume of the human brain (particularly after expansion). For instance, Lillvis et al. utilized 8-fold expansion and lattice light-sheet microscopy (ExLLSM) to image the Drosophila central complex in three colors with effective 30×30×100 nm voxels over the course of 5 days.⁶ But even accounting for the 8-fold expansion (512-fold volume increase), this amounts to a volume of less than 0.5 mm³. Imaging even a 4-fold expanded mouse brain assuming these numbers would take 876 years. Lattice light-sheet microscopes are relatively inexpensive at a few hundred thousand dollars each⁷ and thus might be parallelized enough to image an entire mouse brain within a year. However, the 2400-fold larger volume of the human brain relative to the mouse brain indicates that it is probably not reasonable to expect success in human connectomics through ExLLSM.

Based on these calculations, I suggest that a radically different strategy is needed for to put the goal of human brain connectomics within reach. Uniting synchrotron x-ray microtomography (XRM) (Figure 1A-B), expansion microscopy (ExM), a recent staining method known as Unclearing Microscopy⁸ may facilitate “ExxRM” imaging of the human brain at sub-100 nm voxel size on timescales of around 1 year for a cost of around $10M. Success of this approach will necessitate overcoming some technical hurdles, yet I am optimistic that these particular challenges can be conquered. ExxRM may represent a feasible platform to acquire images suitable for dense connectomics across entire human brains.

Figure 1 Principles of synchrotron XRM. (A) Synchrotrons generate electrons from a source, propel them through a linear accelerator (linac), raise their energy in a booster ring, and then keep the electrons circulating for long periods of time at relativistic speeds in the storage ring. As relativistic electrons move along a curved path controlled by bending magnets, they emit brilliant x-ray beams in the direction tangent to the curve in the direction of the electron movement. Insertion devices that inject the x-rays into beamlines are placed at straight sections of the synchrotron ring. These insertion devices stimulate emission of bright and coherent beams into the experimental stations. (B) Microtomography beamlines receive an x-ray beam from an insertion device and filter out a narrow band of wavelengths using a monochromator. The beam passes through a sample on a rotating tomography stage that can be positionally adjusted to change the location of the field of view within the sample. Projection images are taken across 360° of rotation. X-rays passing through the sample are converted to visible light using a scintillator, then directed by mirrors to a lens system that magnifies the image. This light hits a detector camera and data is recorded for 3D reconstruction.

Recommended methodologies for ExxRM

ExxRM’s success will require developing methodological strategies to mitigate technical challenges. Obtaining sufficient contrast to resolve subcellular features will be vital. ExM cubically dilutes the amount of cellular material per unit volume, so creative staining techniques will be needed. Approaches that ensure stability of expanded tissues under brilliant x-ray illumination for long durations will also be crucial. Multicolor imaging would greatly benefit the usefulness of whole brain connectomics data, so ways of efficiently obtaining tomograms in multiple colors are needed. These challenges must be conquered to translate ExxRM.

A central engineering hurdle for ExxRM is attaining sufficient feature contrast for capture of clear images. Because expanded tissues experience cubic dilution of target biomolecules, contrast from traditional stains such as osmium tetroxide almost entirely vanishes during x-ray microtomographic imaging (Collins et al., unpublished data). An elegant solution to this problem has come in the form of a recently developed technique called Unclearing Microscopy.⁸ For this technique, M’Saad et al. biotinylated primary amines (found on proteins, phosphatidylethanolamine lipids, etc.) throughout expanded samples, treated the sample with streptavidin horseradish peroxidase (streptavidin-HRP) fusion protein, and then stained with ionic silver reagents (from the EnzMet™ HRP Detection Kit) or with 3’3-diaminobenzidine (DAB). This triggered enzymatic deposition of enough chromogenic silver or DAB to make 20-fold expanded HeLa cells visible to the naked eye despite their 8000-fold increase in volume relative to the unexpanded state. Phase contrast light microscopy subsequently revealed subcellular features such as mitochondrial cristae, nuclear pore complexes, and nuclear membrane. Unclearing thus facilitates physical reconstruction of the structures that are pulled apart by ExM, filling in the gaps left by the expansion process (Figure 2A-B). Silver stain Unclearing could enable either absorption XRM or phase contrast XRM of expanded tissues since silver has excellent x-ray attenuation index β at relevant beam energies as well as excellent x-ray phase decrement δ. Focusing on phase contrast XRM may represent a better option since it can decrease the necessary dose of radiation per unit time by using x-ray wavelengths which are not absorbed as strongly by the tissue. Furthermore, phase contrast XRM is most sensitive to differences in sample density⁹ and silver staining forms dense precipitates of metallic silver (which in pure form has a high density of 10.49 g/cm³), so this approach might provide superior contrast in the context of ExxRM. Here, ExM’s sample dilution might prove advantageous because it could generate strongly distinct densities between silver-stained cellular features and the rest of the stabilized hydrogel. It should be noted that, for phase contrast XRM, the previously mentioned stabilization approach would need to fill the space between cellular features with a substance that differs substantially in density from the silver (or similar stain). Combining Unclearing Microscopy with phase contrast XRM could provide excellent feature contrast for ExxRM connectomics.

Figure 2 Proposed sample preparation technique for ExxRM. (A) Macroscale view of a human brain undergoing expansion, Unclearing, and stabilization. Slicing into centimeter-scale subvolumes is not shown but may also represent a beneficial strategy. (B) Nanoscale view of a neuron within the brain undergoing expansion, Unclearing, and stabilization. Considerable amplification of signal density after Unclearing should occur.

Expanded tissues are known for their fragility and synchrotron x-rays are known for their harshness, so strategies for solving the problem of sample degradation are needed. Newer expansion recipes which use high monomer concentrations have displayed substantially greater physical sturdiness than earlier generations of ExM hydrogels,^10,11 so this may aid in stabilization to some degree. Yet additional advances in ExM sample preparation may still be necessary due to high required fluxes and long imaging times. Cutting human brain samples into smaller volumes on the order of a few centimeters may improve the situation by decreasing imaging times per sample. Keeping the sample at cryogenic temperatures with the help of cryoprotectants to prevent ice crystals damaging the tissue may also help since heat generation from the x-rays is particularly problematic for hydrated samples.^12,13 That said, low temperatures would not fix the issue of radiation damage from ionization, so this issue should still be considered.¹⁴ Further stabilization might come in the form of infusion of a rapidly crosslinkable or crystalizing substance which irreversibly locks all biomolecules into place (Figure 2A-B). The chosen substance would need to undergo an inducible crosslinking or crystallization reaction that does not cause distortion of the expanded tissue, to not shrink or distort the expanded tissue while it diffuses into the hydrogel, and to minimally absorb x-rays at the energy range used for imaging, and to contrast sharply with the chosen stain material. The phase decrement of the stabilizing substance should be similar or lower compared to amorphous ice. These methods may enable sufficient stabilization for preventing nanoscale tissue degradation even with exposure to extremely brilliant synchrotron x-rays.

Multicolor ExxRM would greatly enhance the value of acquired image data since synapses and key biomolecules could then be tagged within the 3D reconstruction.^15,16 Fortunately, multicolor absorption x-ray microscopy represents an established technique wherein two or more staining materials undergo two or more rounds of imaging at beam energies corresponding to the absorption edges of the chosen materials. The absorption edges in question must be sufficiently distinct that minimal overlap in detection occurs across the different beam energy imaging rounds. As an example, Depannemaecker et al. employed this approach by using antibody-linked gold nanoparticles to mark neuronal nuclei in mouse brains while also staining neurons with silver via Golgi’s method.¹⁵ They imaged at a beam energy corresponding to an absorption edge of silver and at a beam energy corresponding to an absorption of gold. In addition, multicolor phase contrast x-ray imaging might be accomplished by using molecularly targeted tags with distinct densities relative to the Unclearing silver stain. For instance, gold has a density about twice that of silver, so it might be possible to segment gold nanoparticles after data acquisition. A potential challenge for these methods comes from the difficulty of inducing reliable diffusion of large metallic nanoparticles into tissue. That said, post-expansion pre-stabilization staining could help overcome the problem since expanded gels are typically more porous than pre-expansion tissue. Multicolor ExxRM in 2-3 colors should be achievable, opening doors to more useful whole-brain image datasets.

Though some trial and error will doubtless prove necessary for ExxRM optimization, the required technologies lay within fairly close reach. High contrast could be achieved by applying Unclearing Microscopy to counter signal dilution from expansion. Stability under brilliant x-rays may be possible by leveraging stabler expansion recipes with higher monomer concentrations, by cutting the brain into centimeter-scale subvolumes to decrease radiation dose per sample, by employing cryogenic temperatures during imaging, and by developing rigidity-enhancing materials to infuse into expanded gels. Multicolor ExxRM could be achieved by staining with metallic nanoparticles linked to affinity reagents and performing additional scans tuned to the absorption edges of the chosen metals. These directions may provide a path towards successful ExxRM.

How fast can synchrotrons image expanded brains?

Synchrotron facilities offer bright and coherent x-rays that can rapidly image large volumes of tissue at high resolutions (Figure 1A-B). For example, Bosch et al. employed synchrotron XRM at the Swiss Light Source (SLS) and the Diamond Light Source (DLS) to acquire 3D reconstructions of an entire mouse olfactory bulb with 325 nm voxel size.¹⁷ In their study, tomograms of approximately 1 mm³ volume took around 20 minutes to acquire. Another investigation by Dyer et al. used the Advanced Photon Source (APS) synchrotron to image a region of mouse cortex at 650 nm voxel size and 1.47 mm³ volume. Imaging this volume took only 6 minutes.¹⁸ Walsh et al. leveraged the European Synchrotron Radiation Facility Extremely Brilliant Source (ESRF-EBS) to image a variety of whole human organs at low resolution and subvolumes within human organs at higher resolution.¹⁹ Notably, they reconstructed a cylinder with ~57 mm³ volume (5.4 mm diameter and 2.5 mm height) within human spleen at 1290 nm voxel size over the course of 2 hours. Ding et al. used the APS to image larval zebrafish with 743 nm voxels and 1.5 mm³ volumes per tomogram in 20 minutes with monochromatic x-ray scans and in just 20 seconds with polychromatic “pink beam” x-ray scans.²⁰ Despite their much higher flux and correspondingly much shorter imaging times, the polychromatic scans unfortunately showed lower resolution due to poor SNR. It should be noted that resolution represents a distinct concept from voxel size and that contrast and SNR play major roles in the final resolution. That said, voxel size can act as a rough proxy for comparisons across different imaging setups assuming that contrast and SNR are consistent. In another key example, Foxley et al. utilized the APS to image whole mouse brains (11.7×11.7×17.7 mm) over the course of 7 hours of acquisition time with 1117 nm voxels.²¹ Finally, Rodgers et al. used the SOLEIL synchrotron and an alternative technique for tomographic acquisition (discussed in more detail later) to image an entire mouse brain a 650 nm voxel size in 8 hours.²² Though the parameters for image acquisition vary considerably across different biological samples and synchrotron hardware setups (table 1), these examples illustrate the power of synchrotron imaging for mapping biological tissues.

Table 1 Specifications of synchrotron tissue imaging experiments from selected references. 

While the voxel sizes described above are not small enough for nanoscale connectomics, incorporating ExM into the sample preparation pipeline presents the possibility of attaining sufficient resolutions for tracing neurites. Expansion factors of around 4x, 10x, and 20x are respectively possible with standard ExM,^23,24 some of the newer enhanced ExM protocols,^10,25,26 and iterative ExM methods.^11,27 Expansion factor can also be intentionally decreased somewhat by adjusting salt concentration. It is important to note that not all ExM protocols retain lipid membranes, so selecting a recipe that preserves such cellular boundaries^10,11,28 will be vital for morphology reconstrction. Balance between expansion factor and imaging time will represent a key determinant of the success of ExxRM for whole brain imaging. Higher expansion factors increase resolution linearly while increasing imaging time cubically. Synchrotron imaging’s extreme speed has the potential to keep up with such volumetric increases, though even this has certain limits.

Du et al. offer a helpful mathematical model to estimate the number of photons needed for imaging a sample with sufficient contrast to distinguish between voxels of desired size.¹² The model starts by considering how many photons n̄_pixel are needed to image a single pixel on a 2D tomographic slice. The pixel is assumed to contain a “feature” material with a phase decrement δ_f that must be distinguished against a background with a phase decrement δ_b. Distinctions can be made between adjacent background pixels b, overlaying background pixels b_o’ and underlaying background pixels b_u’. The SNR is assumed to equal 5 as based on the Rose criterion, which qualitatively represents an acceptable SNR for visually useful images. Background attenuation coefficient is given by µ_b’ and the thicknesses of the overlaying and underlaying background material are t_b’o and t_b’u respectively. After the value of n̄_pixel is determined, the value is multiplied by the square N² of the number of pixels N on the detector’s horizontal axis to find the number of photons needed per tomogram. As Du et al. explain, the number of slices is not used as a multiplicative factor since the photons are distributed across all of the rotation angles for each pixel. These calculations act as a guide to what sample properties and beam properties are needed for ExxRM.

The Du et al. model can help determine the feasibility of ExxRM by estimating the necessary synchrotron flux for imaging the brain at a desired voxel size in a reasonable time frame. Consider 1 cm³ of brain tissue expanded 4-fold to a block with dimensions of 4×4×4 cm. Based on this, assume that the average thickness of overlaying and underlaying background material is 2 cm. Also assume that the background material is made of amorphous water ice with a density of 0.92 g/cm³. Since the widths of the smallest neuronal features are about 20 nm wide,²⁹ assume that the 4-fold expansion provides a minimum feature thickness of 80 nm. By using the Unclearing Microscopy technique, such features might be “filled in” after expansion with a metallic silver stain.⁸ Thus, I will assume that the 4-fold expanded membrane features are made up of metallic silver with a density of 5.25 g/cm³, half the density of pure metallic silver (to account for imperfect staining). Feature phase decrements δ_f, background phase decrements δ_b, and background attenuation coefficients µ_b’ across x-ray energies ranging from 10 keV to 30 keV can be obtained using the calculator available at (https://henke.lbl.gov/optical_constants/pert_form.html) which is based on the Henke dataset.³⁰ This calculator does not directly give µ_b’, but it does give attenuation index β, for which µ_b’ = 4πβ/λ. I will furthermore assume that a 3×3 mm beam and detector are possible to employ and that the pixel size is 300 nm (75 nm effective post-expansion). According to the resulting model, the total number of photons needed to image a single tomogram (volume of 21.21 mm³) within the 4-fold expanded block at an energy of 30 keV is 1.47×10¹³. By examining this model across a range of x-ray energies (Figure 3A) and across a range of feature sizes (Figure 3A), one can obtain a better understanding of how the different variables might play out in an experimental setting.

Figure 3 (A) Total photons needed to image a single tomogram of 4-fold expanded tissue with volume of 21.21 mm³ at 300 nm voxel size and two different silver stain densities while assuming feature thickness of 80 nm, plotted against x-ray energy in keV. (B) Total photons needed to image a single tomogram of 4-fold expanded tissue with volume of 21.21 mm³ at 300 nm voxel size and two different silver stain densities while assuming 30 keV x-ray energy, plotted against desired feature thickness necessary to resolve.

How might this translate to imaging time for a single tomogram, the 4×4×4 cm expanded block, and the whole human brain? Contemporary beamlines which employ multilayer monochromators can reach fluxes of more than 10¹² photons/s.^31–35 As such, the single tomogram under the assumptions of the model might be acquired in around 10 seconds. Comparing the tomogram volume of 21.21 mm³ and the expanded block volume of 64000 mm³, this means that the whole block would require about 3000 tomograms and take 8.3 hours to image. Since the entire human brain should contain approximately 1200 of these blocks, whole-brain acquisition might take around 1.14 years of continuous imaging. All of this assumes the post-expansion effective voxel size of 75 nm (4-fold expansion and actual voxel size of 300 nm), 30 keV beam energy, and Unclearing of neuronal features to a density of 5.25 g/cm³ metallic silver. If these theoretical projections successfully align with experimental outcomes, ExxRM could facilitate nanoscale connectomics for the entire human brain in a time frame of around 1-2 years.

Cost estimates for whole brain ExxRM

While the power of the synchrotron facility comes with a high price tag, ExxRM may still represent the most overall cost-effective option for human brain connectomics. Consider the costs associated with the DLS as an illustrative example. The DLS is a third-generation facility and is currently one of the better synchrotrons in terms of its ability to produce bright and coherent x-rays. Building the DLS and its first seven beamlines from 2003-2007 cost about $316M, its later upgrades cost $144M and $134M, and its yearly operational costs have increased from $28M in 2007-2008 to $81M in 2019-2020 (table 2). Based on these data points, construction of a new synchrotron beamline costs approximately $10M and yearly maintenance may cost roughly $500K. This provides a framework for estimating the cost of a dedicated human brain ExxRM connectomics beamline.

Table 2 Costs associated with the Diamond Light Source³⁶ as a case study on how much money is needed to build and maintain a state-of-the-art synchrotron facility.

Compact Light Source (CLS) technology should be considered as well before continuing. CLS instruments produce x-ray beams that fall somewhere between laboratory x-ray microscopes and synchrotrons in terms of brightness and coherence.³⁷ CLS instruments are furthermore small enough to fit into a single room and are inexpensive enough that a large number of them could potentially be constructed in parallel. At first glance, CLS technology seems a more economically viable alternative to synchrotron beamlines, yet it still probably is not a good option at this time. Existing CLS instruments are not likely suitable for human brain (or even mouse brain) ExxRM connectomics in the foreseeable future because their optical engineering requirements and mediocre level of x-ray flux preclude rapid tomography at submicron resolution, particularly when a large field of view is desired.³⁸ There is a small possibility that future advances in CLS technology could change this situation, yet this seems fairly unlikely, so synchrotron-based imaging remains the best route.

Building an entire synchrotron solely for ExxRM connectomics is probably less efficient than establishing an agreement with an existing synchrotron to construct a connectomics beamline. While one might envision additional parallelization through custom design of the beamline to split the beam to land in multiple sample chambers, splitting the beam would divide the photon flux and therefore increase imaging times for no net gain in speed. As such, parallelization would likely require an additional insertion device and thus an entire new beamline for each new sample chamber. Insertion devices consist of a series of precisely engineered magnets built into straight sections of the synchrotron’s ring. These magnets, known as undulators or wigglers depending on the specifics of the insertion device, stimulate directed emission of a brilliant x-ray beam out from the storage ring.³⁹ Yet even if we assume $10M total plus yearly maintenance costs for each connectomics beamline, imaging multiple human brains over the course of a year or a single human brain in just a few months remains a reasonable proposition. For a project as important as mapping the human brain at sufficient resolution for dense neuronal reconstruction, price tags in the range of tens of millions of dollars may not be out of reach.

Conclusion and outlook

Connectomics needs a technological paradigm shift if it is to feasibilize dense mapping of one or more human brains. Though it comes with some technical challenges, ExxRM may represent a key strategic shift that drastically reduces human brain connectomics timelines from centuries down to 1-2 years for the image acquisition step. Data storage and early processing steps (e.g. tomographic reconstruction) will of course require data centers and high-performance computing, but this field is rapidly advancing and may indeed be capable of handling the challenge. Assuming 1 byte per voxel, the amount of storage needed for a 4-fold expanded human brain with 300 nm physical voxel size (75 nm effective voxel size) is about 2.84 exabytes. Segmentation of the human brain dataset will probably represent a vastly more difficult problem as well as require substantially more compute resources, so further advances in this area will need to occur in parallel with ExxRM development. Realizing the benefits of connectomics in the form of complete computational models of the brain will take additional extensive research. Precisely correlating gene expression and electrophysiological properties with neuronal morphology (i.e. “cell type”) may represent a major step towards bridging the divide between structural data and functional activity. Nonetheless, the allure of having a holistic anatomical picture of the brain may serve as a driving force in the meantime. ExxRM has the potential to transform the dense connectomics field, enabling anatomical imaging of the entire human brain with sub-100 nm voxel size and high contrast in around 1-2 years for a price of roughly $10M.

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As a scientist, I am driven by the power of technological breakthroughs to make positive change for humanity. While I also take immense pleasure in the artistic/creative aspects of technology design, my motivation is centered on helping people and on protecting the future of the human species. For this reason, I am interested in a wide array of contemporary challenges as described in this outline. Because I am a synthetic biologist and synthetic biology has many applications, I have the ability to explore solutions to such diverse challenges despite their highly multidisciplinary nature.

That said, one of the tools in any good researcher’s repertoire is collaboration. Since I am just one person, my knowledge can only go so deep in so many areas. Interdisciplinary projects are much more likely to succeed when experts from multiple areas work together. So, I leverage collaboration extensively when carrying out my projects and will continue to do so in the future.

It should be noted that, though I am publicly presenting a number of conceptual explanations of possible solutions to important problems via this list, I have deliberately stated them in somewhat vague language to prevent their public disclosure from precluding outside investment.

I am sharing this list as a way of increasing my likelihood of connecting with potential collaborators over time and as a method of inspiring others to consider how they too might contribute to building a bright future.

I chose the featured image of a Martian-hued rendering of the Anasazi Cliff Palace at Mesa Verde National Park as a symbolic tribute to the creative and culturally rich spirit of humanity, an homage to where we have come from, and a hopeful indication of where we might go in the future.

Infectious Disease Burden in Developing Countries

• Especially malaria, tuberculosis, and HIV.
• Translational strategies for dissemination are key.
• Some possible solutions:
  • Gene drives to prevent mosquitos from carrying pathogens
  • Inexpensive home diagnostics
  • Rapid inexpensive biomanufacturing of treatments
  • Thermostable treatments and vaccines
  • Inexpensive immune enhancement gene therapies
  • Rapid inexpensive biomanufacturing of vaccines

Antibacterial Resistance

• Especially highly concerning pathogens such as carbapenem-resistant Enterobacterales, carbapenem-resistant Acinetobacter, Clostridioides difficile, and drug-resistant Neisseria gonorrhoeae.
• Adaptability and rapid generation of new solutions are key.
• Some possible solutions:
  • Adaptable probiotic treatments
  • Rational design of phage therapies
  • Diversified phage therapy production platforms
  • Lysogenic phage therapies for widespread resistance shutdown and/or bactericide
  • Sentinel bacteria for detection and elimination of resistance in environment (esp. livestock and wastewater)
  • Bacterial conjugation for elimination of resistance in environment
  • Immune enhancement gene therapies
  • Rapid inexpensive biomanufacturing of vaccines
  • Rapid vaccine discovery platforms
  • Inexpensive home diagnostics

Affordable Gene Therapy Manufacturing

• Many existing gene therapy delivery vehicles are extremely expensive to produce, so treatments are very costly for patients and not enough can be made to reach large populations.
• Novel manufacturing solutions are vital to scalably make existing vectors (esp. AAVs).
• New vectors which retain the benefits of existing vectors yet can be made inexpensively are also important.
• Some possible solutions:
  • Synthetic biology methods for radically redesigning cellular platforms of virus production
  • Novel inexpensively manufacturable viral vectors
  • Hybrid nanoparticle-viral vectors that are easier to produce yet retain benefits of viruses and perhaps bring extra advantages as well
  • Methods for production of DNA origami nanorobots
  • Use of DNA origami to support viral capsid assembly and genome packaging

Gene Therapy for Radiation Resistance in Space

• Extended periods of time in space, on the moon, on Mars, etc. expose people to large amounts of radiation.
• Future space colonization efforts could be severely jeopardized by human radiation exposure, especially since this can cause problems with reproduction.
• Some possible genetic approaches:
  • Add genes derived from radiation-resistant organisms (e.g. tardigrades, Deinococcus radiodurans, etc.)
  • Enhance human DNA repair pathways by adding new genetic circuits and/or altering gene regulation
  • Polygenic gene therapy delivery vectors
  • Develop ways of safely delivering genes to most or all cells in the human body

Gene Therapy for Aging

• Aging affects everyone and is marked by a deterioration of health and eventual death.
• Treatments for aging would greatly improve the human condition by making people both much healthier and longer lived.
• Life extension only linearly affects population growth, whereas reproduction exponentially affects population growth, so concerns about life extension causing overpopulation are often exaggerated.
• As human longevity increases, its small contribution to population growth will likely be mitigated by parallel growth of technologies that improve human sustainability (e.g. vertical farms, cultured meat, renewable energy, space habitation, etc.)
• Gene therapy has great potential for extending human longevity and simultaneously improving overall global health.
• Some possible genetic approaches:
  • Polygenic gene therapy delivery vectors
  • Identify multiple genes that synergistically improve healthspan and deliver them together
  • Engineer regulatory pathways and insert genetic circuits that optimally modulate gene expression for improving healthspan

Biological Carbon Capture for Climate Change

• The climate crisis threatens human and nonhuman life since is leading to desertification, flooding, extreme weather events, ecosystem collapse, etc. and will cause many millions of deaths if it continues unchecked.
• Addressing climate change will necessitate both policy solutions and technological solutions.
• Carbon capture is a particularly promising route towards mitigating climate change yet is difficult to scale.
• Self-replicating biological carbon capture approaches would come with risks but demonstrate much greater scalability.
• Some possible biological carbon capture solutions:
  • Genetically enhanced trees that more rapidly capture carbon
  • Genetically enhanced cyanobacteria (or algae) that more rapidly capture carbon
  • Bacteriophages that propagate genes in ocean cyanobacteria to enhance their carbon uptake
  • Use bacterial conjugation in ocean cyanobacteria to propagate genes that enhance their resistance to bacteriophages and thus rapidly increase cyanobacterial population size and carbon capture capacity
  • Develop computational models and experimental model systems to explore possible negative side effects of all of the above, find ways to counterbalance those side effects

Strong Nanorobotics

• Strong nanorobots with capabilities resembling those in science fiction would revolutionize every human activity.
• Strong nanorobots should specifically possess (1) inexpensive mass producibility or self-replication capabilities, (2) ways of programming them to alter their functionality and environmental responses, (3) automated locomotion oriented towards accomplishing programmed tasks.
• Only relatively simple nanorobots have been created so far.
• Some possible routes towards strong nanorobotics:
  • Develop minimal cells derived from Mycoplasma bacteria, equip them with extensive optogenetic equipment for external programming, keep the “programming mode” turned off except when a special chemical switch is flipped to prevent light from scrambling their instructions
  • Synthesize complex dynamic DNA origami nanostructures that include optically programmable logic systems inspired by digital logic circuits as well as automated locomotion modules, also devise scalable manufacturing methods

Horizontal Gene Drives to Repair Pollinator Insect Networks

• Bees and other insect pollinators form a crucial part of global ecosystems, yet populations of these insects are declining.
• Decline of insect pollinators is negatively affecting many crops, limiting food production across the world.
• Some of the most prominent reasons for bee decline are the spread of invasive Varroa mites that carry deformed wing virus (DWV) and the prevalence of toxic pesticides in the environment.
• Horizontal gene transfer via seeding donor bacteria into insect gut microbiota may help protect insect pollinators in a scalable fashion.
• Some possible approaches involving horizontal gene drives:
  • Give bees gut bacteria that spread conjugative plasmids carrying anti-Varroa genes.
  • Give bees gut bacteria that spread conjugative plasmids carrying anti-DWV genes.
  • Give bees gut bacteria that spread conjugative plasmids carrying genes that facilitate breakdown of toxic pesticides.

Connectomics Towards Whole-Brain Emulation

• Mapping the brain and simulating it in a computer would provide an unparalleled holistic understanding of how our minds work.
• Even partial connectomes and/or animal connectomes could give remarkable insights into neurobiology.
• The convergence of connectomics and computational neuroscience has applications in bioinspired artificial intelligence, bioinspired robotics, neural prostheses, medicine for brain disease, and more.
• Some possible routes for connectomics:
  • Expansion microscopy with genetically encoded synaptic and neuronal barcodes coupled with spatial transcriptomics
  • Massively parallel electron microscopy approaches
  • Improved x-ray nanotomography coupled with special sample treatment methods to improve tissue stability, allow multicolor imaging (and possibly barcodes), and enhance resolution
  • Construction of many compact light source devices to rapidly map tissue in parallel
  • Construction of extremely bright next-generation synchrotrons coupled with sample treatment methods to greatly improve stability

Nanobiotechnology for Neural Interfaces and Neural Prostheses

• Existing neural interfaces and neural prostheses utilize microelectrode-based technologies for communicating with brain tissue.
• Nanobiotechnology approaches may enable more precise, less invasive, and more powerful neural interfaces and prostheses.
• Some possible approaches:
  • Polymersome nanocompartments that mimic neurons in their electrical response properties via embedded transmembrane proteins and ion gradients, link to nanowires that transmit electrical potential to external devices or other parts of the brain
  • Leverage gene therapy for modifying human neurons to express optogenetic channels, design nanomachines that incorporate upconversion nanoparticles for targeted stimulation

Terraforming

• The future of humankind depends on our ability to colonize other planets, moons, etc.
• Earth is the only planet in the solar system where humans can survive unaided.
• Terraforming the moon or Mars would provide humankind with a new habitable world, yet this represents an enormously challenging task.
• Seeding the moon or Mars with heavily engineered microorganisms may provide a first step towards transforming these celestial bodies into habitable worlds.
• Some possible microorganism-based terraforming methods:
  • Perform extensive metabolic engineering on extremophiles that metabolize substances similar to those found on the moon or Mars, make them convert regolith to Earthlike atmospheric gases
  • Borrow genetic pathways from Deinococcus radiodurans to provide radiation resistance
  • Extensively engineer microorganisms to metabolize regolith and form seaweed-like colonies that bud into edible fruits