Engineered multicellular neuronal ciliated organisms

Abstract

Disclosed are engineered multicellular organisms termed neurobots. The disclosed organisms comprise an aggregate of ciliated cells and an aggregate of neuronal cells, and the organisms move when the ciliated cells are actuated, e.g., by the neuronal cells. Also disclosed are systems and methods for designing, preparing, and utilizing the neurobots.

Description

ENGINEERED MULTICELLULAR NEURONAL CILIATED ORGANISMSCROSS-REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 594,270 filed on October 30, 2023. The contents of which is incorporated by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under grant HR0011-18-2-0022 and W91 INF 1920027 both awarded by the Department of Defense. The government has certain rights in the invention.FIELD

[0003] The field of the invention relates to engineered multicellular organisms and systems and methods for designing, preparing, and utilizing engineered multicellular organisms. The engineered multicellular organisms may be configured for movement and other physical and biological activities.BACKGROUND

[0004] The formation and architecture of biological neural networks in animal models have arrived at their current structure-function relationship through evolution by natural selection. The development, or control, of such structure-function relationships in a scenario where neurons from these animals are placed inside a new motile body and allowed to grow is unknown. To date motile biological robots (biobots) have been built out of various cell types including skin or muscle tissues, but they have not yet incorporated biological neural networks. Likewise, neuroncontaining biobots have always been made of scaffolds with neurons connecting in an engineer- specified manner. Autonomous self-assembly of brand new nervous system structures, and their control of biobot function (especially via ciliary, not muscle motion), have not been exploited.SUMMARY

[0005] Disclosed herein are engineered multicellular organisms termed “neurobots". The neurobots comprise an aggregate of xenopus neuronal and ectodermal cells, which are capable of movement as a result of the spontaneous beating of the cilia on the multicilaited cells. Also disclosed are systems and methods for designing, preparing, and utilizing the neurobots.

[0006] One aspect of the present disclosure provides an engineered multicellular organism comprising an aggregate of ciliated cells and neuronal cells, wherein the organism moves when the ciliated cells are actuated. In some embodiments the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold. Tn some embodiments the organism comprises a sensor for detecting a target molecule. In some embodiments the cells of the organism self-assemble. In some embodiments the neuronal tissue comprise neurites, optionally, wherein the neurites extend to the ciliated cells. In some embodiments the ciliated cells, the neuronal cells, or both are engineered to express a heterologous molecule. In some embodiments the aggregate of cells is response to light. In some embodiments the organism is configured for moving a target obj ect. In some embodiments the plurality exhibits collective and / or coordinated behavior.

[0007] Another aspect of the present disclosure provides a method for removing a target substrate from an environment. In some embodiments, the method comprises engineering an organism described herein or a plurality thereof to express an enzyme that metabolizes the target substrate and placing the organism in the environment. In some embodiments a method for detecting a target ligand in a sample is provided. The method comprising engineering an organism described herein or a plurality thereof to express a receptor for the target ligand and place the organism in the sample, wherein the organism generates a signal after the receptor binds the target ligand. In some embodiments a method for detecting movement of an organism described herein, wherein the organism or the plurality thereof expresses a photoconvertible fluorescent reporter molecule, and the organism generates a fluorescent signal when the photoconvertible fluorescent reporter molecule is exposed to light is provided.

[0008] Another aspect of the present invention provides a method for preparing the engineered multicellular organism described herein. The method comprises explanting cells fromtissue and culturing the explanted cells under conditions in which the cultured, explanted cells form the engineered multicellular organism or the plurality thereof.

[0009] Another aspect of the present invention provides a system comprising an engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, the system comprising a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when the ciliated cells are actuated and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form a multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated. In some embodiments, the engineered multicellular organism is semitoroidal in shape. In some embodiments the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object. In some embodiments the organism is configured to have a cavity for capturing and / or transporting a target object. In some embodiments the organism comprises amphibian cells.

[0010] Another aspect of the present invention provides a method for forming an engineered multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated. The method comprising combining in cell media a first engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, the system comprising a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when the ciliated cells are actuated, optionally wherein the neuronal cells direct or influence the actuation of the ciliated cells, and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form the multicellular organism comprising the aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated. In some embodiments the organism does not comprise neural cells or neural tissue. In some embodiments the ciliated cells are eukaryotic epidermal cells. In some embodiments the organism comprises amphibian cells.

[0011] BRIEF DESCRIPTION OF THE FIGURES

[0012] Figure 1. a. Construction and development of a neurobot. Neural precursor clumps were placed in the center of an animal cap ‘cup’, excised from the animal pole of Xetiopus laevisembryo before it fully closes up as it heals. The composite forms into a sphere and gradually into a more elongated shape which is mobile by on day 3. b. Examples of two neurobots, one more round than the other, c. A Roundness Index (RI) was calculated by fitting an ellipse on the image of the bot and calculating the ratio between the minor and major axes. Neurobots tended to be less round than biobots. Non-parametric Kruskalwallis test was used to calculate statistical significance.

[0013] Figure 2. Z-proj ection of confocal image stack of a neurobot labeled with acetylated alpha tubulin (a,b) which stains neurons and cilia of the multiciliated cells, b is the staining of the same neurobot with fewer projected planes, allowing to visualize neural processes inside the bot. Color code corresponds to the depth within the bot (confocal plane number), c-d Subregions of the same neurobot, showing neural processes projecting towards the surface cells. Red shows the acetylated alpha tubulin stain (AAT) and cyan depicts a nuclear (Hoescht) co-label (NUC). Yellow arrows point to neural processes (Neu) or multiciliated cells whos cilia are stained (MCC). White arrows point to nuclear staining (Nuc).

[0014] Figure 3. a. Average fluorescence of a freely moving neurobot containing neurons that express GCaMP6s after motion correction (10 minutes of movement imaged at 5 frames per second). Colored circles correspond to regions of interest identified by suite2p software, which could be single or multiple units, b. Movement trajectory of the same neurobot. c. The top curve shows the X-position of the neurobot over time. The 5 bottom curves show baseline-subtracted fluorescence activity of units labeled in panel a. Arrowheads point to synchronized activity in some nearby and distant ROIs.

[0015] Figure 4. a. Exemplar trajectories of neurobots moving in an 8-well plate over a 30 min trial, b. Details of the trajectories of the same hots as shown in panel a. Color gradient indicates time during the trial.

[0016] Figure 5. a. Examples of simple (top panel) and complex (bottom) trajectories, b. Time series of the movement amplitudes projected on the X axis. c. Power spectral densities corresponding to time series in panel b. Red stars mark the location of significant peaks.

[0017] Figure 6. a. Neurobots tended to have more complex trajectories than biobots. b,c. Complexity index was not correlated with Roundness Index or bot size. d. Neurobots were morelikely to be active than biobots. Non-parametric Kruskalwallis test was used to calculate statistical significance.

[0018] Figure 7. a,b. Examples of neurobots stained with acetylated alpha tubulin which stains multiciliated cells and neurons. Overlaid white curves show the neural processes traced using Imaris software, a is an example of a neurobot with small degree of innervation Nterminais=40, LNeurite=753.8 pm, and b is an example of a neurobot with a high degree of innervation Ntenninais=327, LNeurite=7635.9 pm. c. Pairwise correlation between structural parameters of all stained neurobots and Complexity Index, Nterminais = total number of endings, LNeurite=total length of neurites, LNeuritenorm=total length of neurites normalized to area, NMcc=total number of multiciliated cells on the top surface, NMccnorm= NMCC normalized to area, RI= Roundness Index, Neu / Ect= ratio of the areas of neural implant to ectoderm shell.

[0019] Figure 8. a. Experimental protocol for testing the effect of PTZ on the movement of neurobots and biobots. The movement of the bots was measured in control media, across three transfers for 30 min. The bots were then transferred to a dish containing 15 mM PTZ solution, their behavior was measured for another 30 minutes, b. The effect of PTZ treatment on the trajectory complexity was defined as the complexity index measured while in PTZ, relative to the average complexity measured for three consecutive MMR trials. Although most biobots reduced their complexity index relative to control, the relative complexity index for neurobots was equally likely to increase or decrease. Relative complexity of zero corresponds to the average CI for the three controls. Filled circles indicates neurobots that were cultured in zolmitriptan prior to testing (Fig. 22).

[0020] Figure 9. a. Pearson correlation within and across groups of neurobots (NB), shams neurobots (SH) and biobots (BB). The closer the value to 1, the more similar the expression patterns, b. Principal component analysis of gene expression values. Each dot corresponds to one sample, which contained multiple bots of one kind. c. Histograms of coefficients of variation (CV) in gene counts (FPKM) in neurobots (red), biobots (blue), and sham neurobots (yellow). Neurobots showed a significantly higher variability in their gene counts compared to both biobots and shams, and shams showed a higher variability compared to biobots. Genes with higher counts showed higher degree of variability in their expression when comparing neurobots with biobots and shams (d,e). Dark blue bars mark the bins where the difference in CV was significantly different fromwhat is expected if the ranking of genes were randomly shuffled (see Methods). Similarly, genes with low levels of expression showed lower coefficient of variation than expected by chance, f. Same as d and e but comparing neurobots with shams. Bin values above 0.5 (light blue line) indicate more than half CVs in that bin had higher values in that comparison. The orange horizontal line indicates the average of all fractions regardless of the ordering. Significance was assessed relative to this value using Kruskalwallis non-parametric statistics (see Methods).

[0021] Figure 10. a-c. Distribution of differentially expressed genes between different bot groups. The X-axis shows the fold change in gene expression between samples of different groups and the Y axis shows the statistical significance of the difference. Red dots represent genes that were significantly up (positive values on the X-axis) or down regulated (negative values on the X- axis), green dots represent genes with no significant change.

[0022] Figure 11. a-b. Enrichment analysis performed using Gene Ontology annotations on differentially expressed genes with at least 4-fold upregulation in expression when comparing neurobots with biobots and sham neurobots.

[0023] Figure 12. Enriched pathways (a,c,e) and network connectivity (b,d,f) among genes in Cluster 3, 23 and 1, containing genes that were upregulated in neurobots complared to biobots respectively. Cluster 5 from network analysis of up regulated genes in neurobots compared to shams (g,h). G:profiler was used for pathway enrichment analysis. Gene ID and highlighted circles corresponds to the most relevant Gene Ontology terms (see Methods) with in the cluster. The size of the circles represents the number of genes in each term. The network connectivity was calculated using STRING online tool. The edges indicate both functional and physical protein associations and the line thickness indicates the strength of data support. Nodes with interaction scores with confidence higher than 0.4 are shown.

[0024] Fig. 13. Results of comparative analysis of neurobot overexpressed genes (a) and downexpressed genes (b).

[0025] Figure 14 shows the variability in the shape and structure of sprouting within the neurobots. Despite these differences, we found that in most neurobots, neural processes tended to emanate from a nucleus marked by high density of nuclear stains, presumably corresponding to the implanted clumps of cells (Fig. 2d). Interestingly, these regions were often surrounded by seemingly regions with no nuclear staining (Fig. 2b-d). The presence of this ‘empty space’ is veryintriguing and we hypothesize that the region might be comprised of neural support structures such as extracellular matrix. Support for this hypothesis includes cases where we observed neurites traversing long distances in this ‘empty space’ on a very straight line (Fig. 2b, yellow arrowhead), suggesting the possibility for formation of, linear neural connections can be implemented. Importantly, we also found a significant upregulation in genes encoding ECM proteins (see Figures 11 and 12). Future experiments are needed to fully characterize the nature of this internal space.

[0026] Figure 15. Biobots that lack any neurons show a higher density of MCC. This is consistent with the negative correlation we found between the MCC and neurite density.

[0027] Figure 16. Treatment with zolmitriptan resulted in a significant increase in neural growth, although it did not have a significant effect on any of the behavioral measurements including trajectory complexity. In this group we found a tight correlation between the ratio of neural implant to ectoderm shell and total number of terminals as well as total neural length. We also found a very tight correlation between the number of neuron terminals and the number of multiciliated cells. This result indicates that treatment with zolmitriptan can be used as a method for increasing neural growth and potentially functionality in neurobots. This is consistent with Previous studies had shown that treatment with zolmitriptan, which is a selective 5- hydroxytryptamine (5-HT) 1B / 1D receptor agonist, increased the degree of ectopic (but not native) neural sprouting m Xenopus embryos (Blackiston, Vien, and Levin 2017).

[0028] Figure 17. Transmission Electron Microscopy image showing Goblet cells (GC) and a multiciliated cell (MC) which are responsible for movement of the bots. On the surface, neurobots possess similar cell types that xenbots do, i.e., those present in the tadpole skin.

[0029] Fig. 18. Provides images of neurobot formation and development. See example 5 for more details on the methods. Panels is an image from Ariizumi et. al (2009) and panel B is from Kurd et al (2005). Panels C and D show two different examples of implantation of neural tissue (middle). Panel E shows Z projection of a confocal stack labeling neurons and multiciliated cells. Color code corresponds to the depth in the Z stack.

[0030] Figure 19. Comparison between physical and kinematic parameters between biobots and sham neurobots. Kruskal- Wallis test was used to obtain p-values.

[0031] Figure 20. Examples of Z-proj ected confocal fluorescent images of neurobots.The first two columns show staining of acetylated alpha tubulin, which labels multiciliated cells and neurons (color code represents depth in the confocal stack). The last column shows the nuclear stain in the same neurobot shown on that row, with the same set of plane shown in the middle panel. The first column shows the Z-proj ection of the full stack, where as the next two columns show partial stacks to reveal the interior of the neurobot. All neurobots contain processes within the bot and those that extend towards the surface. They also contain a central region with seemingly no nuclei present, which we hypothesize might be filled with extracellular matrix materials.

[0032] Figure 21 . Neurobots had a significantly smaller density of multiciliated cells. Kruskal- Wallis test was used to obtain p-values.

[0033] Figure 22. a-cTreatment with zolmitriptan increased number of terminals, total length of neurites and neurite density. There was no significant change in complexity index (d). e. Pairwise correlation between structural parameters of zolmitriptan-treated neurobots and Complexity Index, Nterminals = total number of endings, LNeurite=total length of neurites, LNeuritenorm=total length of neurites normalized to area, NMCC=total number of multiciliated cells on the top surface, NMCCnorm= NMCC normalized to area, RI= Roundness Index, Neu / Ect= ratio of the areas of neural implant to ectoderm shell.

[0034] Figure 23. Method for quantifying variability in gene expression. For a chosen pair of groups, genes were ranked by the mean count value across all pools of both groups, and the CV of each gene’s counts across the pools of each group was calculated. The CV list was split into 100 bins (percentiles) containing equal numbers of genes, and the fraction of genes in the bin for which the CV of the first group was greater than that of the second group was found and plotted.

[0035] Figure 24. Enrichment analysis performed using Gene Ontology annotations on differentially expressed genes with at least 4-fold upregulation in expression when comparing a. sham neurobots with biobots (upregulated pathways) and b. neurobots with biobots (downregulated pathways)

[0036] Figure 25. Enriched pathways in neurobots when compared to biobots, across clusters identified based on network analysis.

[0037] Figure 26. Enriched pathways in neurobots when compared to sham neurobots, across clusters identified based on network analysis.DETAILED DESCRIPTION

[0038] The following discussion is presented to enable a person skilled in the art to make and use embodiments of the disclosure. Various modifications to the illustrated embodiments will be readily apparent to those skilled in the art, and the generic principles herein can be applied to other embodiments and applications without departing from embodiments of the disclosure. Thus, embodiments of the disclosure are not intended to be limited to embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein. The following detailed description is to be read with reference to the figures. The figures, which are not necessarily to scale, depict selected embodiments and are not intended to limit the scope of embodiments of the disclosure. Skilled artisans will recognize the examples provided herein have many useful alternatives and fall within the scope of embodiments of the disclosure.

[0039] Biobots generated through culturing ectodermal tissue excised from frog (Xenopus laevis) embryos are autonomous, self-powered, and can move through aqueous environments. Here the inventors report a novel type of biobot that is composed of ciliated epidermis and additionally incorporates neural tissue (Neurobots). The inventors show that neural precursor cells implanted within Xenopus skin constructs develop into mature neurons and extend processes towards surface cells as well as among each other. Preliminary calcium imaging experiments show that these neurons are indeed active. Neurobots offer a unique opportunity to investigate the rules that govern the formation of biological neural networks within a novel environment, and to devise methods for harnessing the computational power of these networks to control and predict behavioral outcomes. Specifically, Neurobots represent a novel extension of the self-assembling biobot field (such as Xenobots and Anthrobots): by containing neurons, they offer the possibility of making useful synthetic living machines with valuable behaviors and information-processing capabilities that go far beyond what bioengineered constructs without self-assembling nervous systems could do.

[0040] Disclosed are engineered multicellular organisms and systems and methods designing, preparing, and utilizing the engineered multicellular organisms. The disclosed subjectmatter may be further described using definitions and terminology as follows. The definitions and terminology used herein are for the purpose of describing particular embodiments only, and are not intended to be limiting.

[0041] As used in this specification and the claims, the singular forms “a,” “an,” and “the” include plural forms unless the context clearly dictates otherwise. For example, the term “a cell” should be interpreted to mean “one or more cells." As used herein, the term “plurality” means “two or more.”

[0042] As used herein, “about”, “approximately,” “substantially,” and “significantly” will be understood by persons of ordinary skill in the art and will vary to some extent on the context in which they are used. If there are uses of the term which are not clear to persons of ordinary skill in the art given the context in which it is used, “about” and “approximately” will mean up to plus or minus 10% of the particular term and “substantially” and “significantly” will mean more than plus or minus 10% of the particular term.

[0043] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.” The terms “comprise” and “comprising” should be interpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims. The terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion of additional components other than the components recited in the claims. The term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.

[0044] The phrase “such as” should be interpreted as “for example, including.” Moreover the use of any and all exemplary language, including but not limited to “such as”, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

[0045] Furthermore, in those instances where a convention analogous to “at least one of A, B and C, etc.” is used, in general such a construction is intended in the sense of one having ordinary skill in the art would understand the convention (e.g., “a system having at least one of A, B and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together.). It will be furtherunderstood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description or figures, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or ‘B or “A and B.”

[0046] All language such as “up to,” “at least,” “greater than,” “less than,” and the like, include the number recited and refer to ranges which can subsequently be broken down into ranges and subranges. A range includes each individual member. Thus, for example, a group having 1-3 members refers to groups having 1, 2, or 3 members. Similarly, a group having 6 members refers to groups having 1, 2, 3, 4, or 6 members, and so forth.

[0047] The modal verb “may” refers to the preferred use or selection of one or more options or choices among the several described embodiments or features contained within the same. Where no options or choices are disclosed regarding a particular embodiment or feature contained in the same, the modal verb “may” refers to an affirmative act regarding how to make or use and aspect of a described embodiment or feature contained in the same, or a definitive decision to use a specific skill regarding a described embodiment or feature contained in the same. In this latter context, the modal verb “may” has the same meaning and connotation as the auxiliary verb “can.”

[0048] Engineered Multicellular Organisms

[0049] Xenobots," are multicellular biological robots ("biobots") from frog (Xenopus laevis) with a diameter ranging from 100 to 500 microns (see e.g., PCT / US2021 / 013105, and PCT / US2022 / 076484, Blackiston et al., "A cellular platform for the development of synthetic living machines," Sci. Robot. 2021 Mar 31;6(52):eabfl571; and Kriegman etal., "Kinematic self- replicatoin in reconfigurable organisms," Proc Natl Acad Sci USA. 2021 Dec 7;1189(49):e2112672118; all of which are incorporated herein by reference in their entireties). Here, we introduce "neurobots," which are distinguished from xenobots by the presence of a plurality of neural cells. Xenobots and neurobots have a built-in capacity for motility in aqueous environments based on locomotive appendages called "cilia" which cover their surface.

[0050] Neurobots are induced to arise by cellular self-organization and do not require scaffolds or microprinting, and the amphibian cells which form these organisms are highly amenable to surgical, genetic, chemical, and optical stimulation to control the self-assembly process. Neurobots are produced by the introduction of neural cells into the self-organizing xenobot. Neurobots are motile and are capable of moving in various trajectories including loops, straight lines, large arcs, and other patterns (Figure 4), with neurobots on average showing significantly more complex patterns compared to xenobots.

[0051] In addition, neurobots can be loaded with exogenous payloads such as RNA, protein, drugs, dyes, and synthetic molecules. While both xenobots and neurobots can be programmed on demand to execute a diverse set of tasks in different environments, the neurobots provide additional ‘control knobs’ and the possibility for generating more complex outcomes through neural control of behavior, thereby providng an advanced platform for programming more complex functions and providing useful work at the microscale such as cleaning contaminants from soils and waterways.

[0052] Without any neurons, xenobots show a range of kinematic behavioral phenotypes which were previously characterized (see e.g., PCT / US2021 / 013105, and PCT / US2022 / 076484). Based on immunohistochemical studies we showed that neuronal precursor cells implanted in animal caps indeed develop into mature neurons, forming a novel type of biomachine, which we call a neurobot. Neurites within neurobots extend throughout the interior, as well as towards the outer shell, which is composed of Xenopus ectodermal cells (Figures 2,14). This includes multiciliated cells which provide the bot with the ability to move, as well as various types of secretory cells, such as mucus-secreting goblet cells (Figure 17). These neurons, therefore, have the potential to modulate the activity of cells lining the exterior of the bot and thereby its behavioral phenotype. Indeed, our functional imaging experiments of neurobots made with neurons that genetically express fluorescent calcium indicators (GCaMP 6s) have confirmed the presence of neural firing and patterns of activity that are consistent with functional connectivity amongst neurons (Figure 3).

[0053] The inventors developed a pipeline for deep quantification of the kinematics variables of neurobots and xenobots’ spontaneously generated trajectories. The inventor allowed the bots to move around 8 well plates for 30 minutes and video recorded their movement fromabove (Figures 4, 5, 6). They then analyzed these videos, automatically tracking the movement of individual bots. This analysis yielded the x-y coordinate of each bot (Figures 4, 5). Based on this information the inventors calculated kinematic variables speed, turn angles, total distance traveled, among others. Additionally, the complexity of the bot movement was calculated by calculating the spectral density of the movement of the bot decomposed into x and y time series (Figure 6). The number of significant peaks in the power spectrum was used as a measure of spectral complexity of the bot. The inventors found that neurobots showed a significant increase in the complexity of their behavior compared to their neuron-less counterpart (Figure 6), suggesting an increase in the ‘degrees of freedom’ because of neural activity.

[0054] Another indication pointing to the ability of neuronal activity to modulate the behavior comes from pharmacological experiments performed by treating neurobots and xenobots with pentylenetetrazol (PTZ), a GABA receptor agonist that is used to create seizures in animal models. PTZ treatment had significantly different effects on the behavior of neurobots and xenobots. Whereas all except for one xenobot decreased their movement complexity upon treatment, 9 out of 16 neurobots showed an increase in complexity upon treatment, whereas the others either did not show a change or decreased (Figure 8). Neurobots wherein neurons failed to develop and sprout, were those that did not show a change in their behavior with PTZ treatment. In contrast, neurobots that showed relatively high neuronal sprouting often showed a positive change, however, two neurobot showed sprouting but no change in behavior. This finding was not unexpected, because in these experiments we did not have any information about the type of neurons that are present within the neurobots. We suspect that those that were most affected likely contained larger proportion of GABAergic neurons.

[0055] Although we have not yet directly demonstrated the presence of functional connectivity between neurons and the movement effectors (e.g., multiciliated cells) these behavioral and pharmacological observations support that hypothesis. Our future experiments aim to directly assess presence of such connectivity, the cell-types that are involved and the consequences different connectivity patterns might have on behavioral phenotypes.

[0056] Neurobot have many differences and advantages to known biobots. For example neurons allow for decision-making, sensing, information-processing, signal discrimination, memory, and learning that only can occur with the presense of neurons. Neurobots also have theability to response to neuroactive drugs and may make neurotransmitters. The generation of neurobots is also unlike xenobots, and presented unique challenges. Making a neurobot involves providing neurons that are still plastic enough to grow new patterns and interact with the other cells. The neurons must also be compatible with the rest of the tissue in order to form connections. Neurobots are also distinguishable from xenobots based on their size and movement in addition to their cellular makeup.

[0057] As used herein an engineered multicellual organism is a living system that is built from biological parts to create functional modules. Examples of an engineered multicellular organism include, but are not limited to xenobots, biobots and neurobots described herein. In some embodiments, the engineered multicellular organism consists of biological material. Biological material may inlcude materials that are produced by living organisms or derived from them. In some embodiments, the engineered multicellual organism does not comprise any inorganic material. Inorganic material may include for example a scaffold or an inorganic support structure. By way of example and not limitation a scaffold may comprise polymer or polydimethylsiloxane scaffolds.

[0058] The neurobots typically comprise an aggregate of ciliated cells (e.g., an aggregate of multiciliated cells) and neuronal cells. In some embodiments, the aggregate of cells comprises, consists essentially of, or consists of epidermal cells, such as ciliated epidermal cells, and one or more neuronal cells. Suitable ciliated cells may include, but are not limited to, ciliated cells derived from ectoderm, (e.g., differentiated ectodermal cells). Suitable neuronal cells include but are not limited to neuronal cells derived from ectoderm (e.g., animal caps), neuronal precursor cells, neural stem cells. In embodiments, neural cells may be from the same species or from a different species. As noted above, so long as the neural cell are compatible with the rest of the tissue in the bot and are sufficiently plastic to grow new patterns and interact with the other cells of the bot, any neural cells or tissue may be contemplated. In embodiments, more than one type of neural cell (e g., different species, different stage of differentiation, different precursors, etc.) may be used.

[0059] Ciliated cells for use in preparing the disclosed engineered multicellular organisms (neurobots) may include ciliated cells which are non-motile in their native condition or tissue but which are motile, or develop or differentiate into ciliated cells in the engineered multicellular organisms. The cilia of the ciliated cells utilized for forming the disclosed engineered multicellularorganisms may be motile cilia, in contrast to non-motile primary cilia. Motile cilia of ciliated cells utilized to form the disclosed engineered multicellular organisms may include an axoneme as known in the art to actuate motility.

[0060] Optionally, the engineered multicellular organisms (neurobots) may meet at least one of the following criteria: (i) the organism comprises less than about 1000 total cells, or less than about 900, 700, 600, 500, 400, 300, 200, or 100 cells (or the organism comprises a number of cells within a range bounded by any of these values (e.g., 100-1000 cells); and (ii) the organism has an effective diameter of less than about 2 mm, or less than about 1.5 mm, 1.0 mm, 0.9 mm, 0.8 mm, 0.7 mm, 0.6 mm, 0.5 mm, 0.4 mm, 0.3 mm, 0.2 mm, or 0.1 mm (or the organism has an effective diameter within a size range bounded by any of these values (e g., 0.1 - 0.5 mm).

[0061] The engineered multicellular organisms (neurobots) preferably are self-motile and move when the cilia of the organisms are actuated. In some embodiments, the cilia of the organisms may be actuated by electrical stimulation or optogenetics where the cilia have been genetically modified to express light-sensitive ion channels. Additionally or alternatively, in some embodiments, cilia actuation is driven by, or influenced by, the neuronal cells of the neurobots.

[0062] In some embodiments, the engineered multicellular organisms (neurobots) move when the cilia of the organisms are actuated. Preferably, the organisms move at a rate of at least about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, 500, or 1000 microns / second or faster when the cilia of the organisms are actuated.

[0063] Preferably, the engineered multicellular organisms (neurobots) have a self-limiting life-span when placed in an physiologically suitable environment of at least about 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 days.

[0064] The engineered multicellular organisms (neurobots) comprise an aggregate of cells which may be referred to as a plurality of living cells that are cohered to one another. The aggregate of cells forms a three-dimensional shape which shape may change over time. Exemplary ciliated cells include, but are not limited to ciliated cells derived from ectoderm (e.g., differentiated ectodermal cells). Additionally, or alternatively, ciliated cell types may include epithelial cells of the bronchi and oviducts or epidermal cells.

[0065] The disclosed organisms also comprise neuronal cells, neuronal cell precursors, and combinations thereof. Neurobots may contain glial cells in addition to neurons. In some embodiments the neuronal tissue comprises neurites. In some embodiments, the neurite extend to the ciliated cells.

[0066] As used herein, neural precursor cells (NPCs) refer to a mixed population of cells comprising undifferentiated progeny of neural stem cells, and in some embodiments include both neural progenitor cells and neural stem cells. The term neural precursor cells is used herein to collectively describe the mixed population of NSCs and neural progenitor cells for example, derived from animal caps. Animal caps are pluripotent cells that can give rise to a variety of cell types depending on signals they receive. Dissociating them and allowing them to develop for 3 hours induces neural fate (Grunz and Tacke 1989; Wilson and Edlund 2001). Such cells are termed herein neural precursor cells.

[0067] Suitable cells for the multicellular organisms (i.e., neurobots) of the present disclosure may comprise animal cells. Exemplary animal cells may comprise fish cells, amphibian cells (e.g., frog cells and the like) or mammalian cells (e.g., human cells, mouse cells, rat cells, and the like). Suitable cells may include cells that have been cultured in vitro.

[0068] In embodiments, the aggregate of cells, and the engineered multicellular organisms may comprise additional non-ciliated cell types.

[0069] The multicellular organisms may be described as "engineered" because they are different from naturally occurring organism that arise without the guidance of human ingenuity and modifications. In other words, the multicellular organisms are synthetic and non-naturally occurring, albeit the multicellular organism may utilize endogenous celkcell signaling and morphogenesis. In some embodiments, the engineered multicellular organism is semitoroidal in shape.

[0070] The aggregate of cells of the engineered multicellular organisms (neurobots) may comprise cells that have been engineered to express a heterologous molecule. In some embodiments, the cells of the organisms are engineered to express a heterologous protein or secrete specific desired molecules. In some embodiments, the heterologous molecule is a reporter molecule.

[0071] In embodiments in which the aggregate of cells comprises cells that have been engineered to express a heterologous molecule, suitable heterologous molecules that are expressed may include enzymes. Suitable enzymes may include enzymes that metabolize a target substrate, which may include toxins. Other suitable heterologous molecules may include receptors for a target ligand (e.g., a target ligand sensed by the organism), or sensors of light, heat, electric field, chemicals, and other physical properties in the environment. Other suitable heterologous molecules may include therapeutic molecules, and reporter molecules (e.g., photoconvertible fluorescent reporter molecules and optogenetic sensors that trigger physiological responses or changes in gene expression).

[0072] In some embodiments, one or more of the cells of the neurobot may express a heterologous molecule. In some embodiments all of the neurons may express a heterologous molecule, or only some of the neurons. In some embodiments, engineered cells may be added to a fully formed or partially formed neurobot.

[0073] The disclosed engineered multicellular organisms (neurobots) may be selfrepairing. In some embodiments, if the aggregate of cells is subjected to deaggregation (e.g., physical damage that disrupts aggregation of the cells), the cells will reaggregate and / or remodel to re-form the aggregate of cells having the original shape or a new shape.

[0074] The engineered multicellular organisms (neurobots) may be configured in order to perform tasks. For example, the engineered multicellular organisms may be configured structurally and / or genetically.

[0075] In some embodiments, the organism (neurobot) is configured for moving a target object (e.g., by pushing a target object). In further embodiments, the organism is configured for moving target objects (e.g., by pushing target objects) and collecting the moved target objections (i.e., aggregating the target objects).

[0076] The engineered multicellular organisms may be configured to have a cavity. In some embodiments, the engineered multicellular organisms (neurobots) are configured to have a cavity for capturing and / or transporting a target object.

[0077] The engineered multicellular organisms (neurobots) may be utilized in a number of applications. In some embodiments, the engineered multicellular organisms are utilized in methodsfor removing a target substrate from an environment (e.g., a toxin from an environment). The methods may comprise engineering the organisms to express an enzyme that metabolizes the target substrate and placing the organism in the environment to remove the target substrate from the environment.

[0078] In other embodiments, the engineered multicellular organisms (neurobots) are utilized in methods for detecting a target ligand in a sample. The methods may comprise engineering the organisms to express a receptor for the target ligand and place the organism in the sample, where the organism generates a signal after the receptor binds the target ligand. Because they contain genes that encode visually responsive proteins, neurobots have the potential to be controlled with light. Also, because they contain neurons, they have the potential to be trained, e g. to become more or less sensitive to a certain stimulus type through non-associative learning paradigms such as habituation or sensitization. They could also be augmented with different types of sensors, potentially responding differently to different stimulus types, e.g. go towards one stimulus and go away from the other. Having neurons makes associative types of learning possible, for example certain connections / behaviors can be strengthened / enhanced through principles of Hebbian plasticity. In this way significantly more complex biobots can be made and they could become more and more complex as more functional neuronal units are added. In this way they have the potential to perform complex tasks such as going to a location, clean up and come back, or they could be used as multi-modal biosensors that would generate a different behavior when they encounter distinct stimuli e.g. toxins or other molecules of interest.

[0079] In other embodiments, the engineered multicellular organisms (neurobots) may express a photoconvertible fluorescent reporter molecule, and the organism generates a fluorescent signal when the photoconvertible fluorescent reporter molecule is exposed to light. As such, the activity and movement of the organisms may be monitored by placing the organisms in an environment and providing a light source for activating the photoconvertible fluorescent reporter molecule.

[0080] Also disclosed are methods for preparing the engineered multicellular organisms (neurobots). In some embodiments, the methods comprising explanting cells from tissue and culturing the explanted cells under conditions in which the cultured, explanted cells form the engineered multicellular organisms.ILLUSTRATIVE EMBODIMENTS

[0081] The following embodiments are illustrative and should not be interpreted to limit the scope of the claimed subject matter.

[0082] Embodiment 1. An engineered multicellular organism comprising an aggregate of ciliated cells, and an aggregate of neuronal cells, wherein the organism moves when the ciliated cells are actuated.

[0083] Embodiment 2. The organism of embodiment 1, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

[0084] Embodiment 3. The organism of embodiment 1 or 2, wherein the organism comprises a sensor for detecting a target molecule.

[0085] Embodiment 4. The organism of any of the foregoing embodiments, wherein the cells of the organism self-assemble.

[0086] Embodiment 5. The organism of any of the foregoing embodiments, wherein the organism has an effective diameter of about 100-500 microns.

[0087] Embodiment 6. The organism of any of the foregoing embodiments, wherein the organism moves at a rate of at least about 15, 20, 25, 50, 100, 200, 500, or 1000 microns / second when the ciliated cells are actuated.

[0088] Embodiment 7. The organism of any of the foregoing embodiments, wherein the neuronal cells comprise neurites, optionally, wherein the neurites contact the ciliated cells, optionally, wherein the neuronal cells actuate or influence the ciliated cells.

[0089] Embodiment 8. The organism of any of the foregoing embodiments, wherein the ciliated cells are eukaryotic epidermal cells.

[0090] Embodiment 9. The organism of any of the foregoing embodiments, wherein the ciliated cells are engineered to express a heterologous molecule.

[0091] Embodiment 10. The organism of embodiment 9, wherein the heterologous molecule is a reporter molecule (e.g., a photoconvertible fluorescent reporter molecule).

[0092] Embodiment 11. The organism of embodiment 9, wherein the heterologous molecule is an enzyme that metabolizes a target substrate (e.g., a toxin).

[0093] Embodiment 12. The organism of embodiment 9, wherein the heterologous molecule is a receptor for a target ligand (e.g., a target ligand sensed by the organism).

[0094] Embodiment 13. The organism of any of the foregoing embodiments, wherein the aggregate of cells reaggregates after the aggregate is subjected to deaggregation (i.e., the organism self-repairs).

[0095] Embodiment 14. The organism of any of the foregoing embodiments, wherein the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object.

[0096] Embodiment 15. The organism of any of the foregoing embodiments, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

[0097] Embodiment 16. The organism of any of the foregoing embodiments, wherein the organism comprises amphibian cells.

[0098] Embodiment 17. A plurality of the organism of any of the foregoing embodiments, wherein the plurality exhibits collective and / or coordinated behavior.

[0099] Embodiment 18. The plurality of embodiment 17, wherein the collective and / or coordinated behavior is collective and / or coordinated movement.

[0100] Embodiment 19. A method for removing a target substrate from an environment, the method comprising engineering the organism of any of embodiments 1-18 or a plurality thereof to express an enzyme that metabolizes the target substrate and placing the organism in the environment.

[0101] Embodiment 20. A method for detecting a target ligand in a sample, the method comprising engineering the organism of any of embodiments 1-18 or plurality thereof to express a receptor for the target ligand and place the organism in the sample, wherein the organism generates a signal after the receptor binds the target ligand.

[0102] Embodiment 21. A method for detecting movement of an organism of any of embodiments 1-17 or a plurality thereof, wherein the organism or the plurality thereof expresses aphotoconvertible fluorescent reporter molecule, and the organism generates a fluorescent signal when the photoconvertible fluorescent reporter molecule is exposed to light.

[0103] Embodiment 22. A method for preparing the engineered multicellular organism of any of embodiments 1-17 or a plurality thereof, the method comprising explanting cells from tissue and culturing the explanted cells under conditions in which the cultured, explanted cells form the engineered multicellular organism or the plurality thereof.

[0104] Embodiment 23. A system comprising an engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, wherein the system comprises a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when the ciliated cells are actuated and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form a multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated.

[0105] Embodiment 24. The system of embodiment 23, wherein the engineered multicellular organism is semitoroidal in shape.

[0106] Embodiment 25. The system of embodiment 23 or 24, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

[0107] Embodiment 26. The system of any of embodiments 23-25, wherein the cells of the organism self-assemble.

[0108] Embodiment 27. The system of any of embodiments 23-26, wherein the organism has an effective diameter of about 100-500 microns.

[0109] Embodiment 28. The system of any of embodiments 23-27, wherein the organism moves at a rate of at least about 15, 20, 25, 50, 100, 200, 500, or 1000 microns / second when the ciliated cells are actuated.

[0110] Embodiment 29. The system of any of embodiments 23-28, wherein the organism does not comprise neural cells or neural tissue.

[0111] Embodiment 30. The system of any of embodiments 23-29, wherein the ciliated cells are eukaryotic epidermal cells.

[0112] Embodiment 31 . The system of any of embodiments 23-30, wherein the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object.

[0113] Embodiment 32. The system of any of embodiments 23-31, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

[0114] Embodiment 33. The system of any of embodiments 23-32, wherein the organism comprises amphibian cells.

[0115] Embodiment 34. A method for forming an engineered multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated the method comprising combining in cell media a first engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, the system comprising a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when the ciliated cells are actuated, optionally wherein the neuronal cells direct or influence the actuation of the ciliated cells, and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form the multicellular organism comprising the aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated.

[0116] Embodiment 35. The method of embodiment 34, wherein the engineered multicellular organism is semitoroidal in shape.

[0117] Embodiment 36. The method of embodiment 34 or 35, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

[0118] Embodiment 37. The method of any of embodiments 34-36, wherein the cells of the organism self-assemble.

[0119] Embodiment 38. The method of any of embodiments 34-37, wherein the organism has an effective diameter of about 100-500 microns.

[0120] Embodiment 39. The method of any of embodiments 34-38, wherein the organism moves at a rate of at least about 15, 20, 25, 50, 100, 200, 500, or 1000 microns / second when the ciliated cells are actuated.

[0121] Embodiment 40. The method of any of embodiments 34-39, wherein the organism does not comprise neural cells or neural tissue.

[0122] Embodiment 41. The method of any of embodiments 34-40, wherein the ciliated cells are eukaryotic epidermal cells.

[0123] Embodiment 42. The method of any of embodiments 34-41 wherein the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object.

[0124] Embodiment 43. The method of any of embodiments 34-42, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

[0125] Embodiment 44. The method of any of embodiments 34-43, wherein the organism comprises amphibian cells.

[0126] Embodiment 45. A multicellular construct comprising a neuronal aggregate surrounded by a plurality of ciliated epithelial cells.

[0127] Embodiment 46. The multicellular construct of embodiment 45, wherein the neuronal aggregate and the ciliated epithelial cells are each derived from an amphibian embryo.

[0128] Embodiment 47. The multicellular construct of embodiment 45 or 46, wherein the neuronal aggregate and the ciliated epithelial cells are each derived from Xenopus embryo.

[0129] Embodiment 48. A motile innervated multicellular construct comprising a surface layer of ciliated epithelial cells and an interior of neuronal cells, wherein the neuronal cells extend processes into the surface layer of the ciliated epithelial cells.

[0130] Embodiment 49. The motile innervated multicellular construct of embodiment 48, wherein the neuronal cells and the ciliated epithelial cells are each derived from an amphibian embryo.

[0131] Embodiment 50. The motile innervated multicellular construct of embodiment 48 or 49, wherein the neuronal cells and the ciliated epithelial cells are each derived from a Xenopus embryo.

[0132] Embodiment 51 . A multicellular construct comprising ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells comprise a heterologous polynucleotide encoding an optogenetic protein, wherein the ciliated epithelial cells express the optogenetic protein and wherein activation of the optogenetic protein modifies actuation of cilia on the ciliated plurality of epithelial cells.

[0133] Embodiment 52. The multicellular construct of embodiment 51, wherein the ciliated epithelial cells are derived from an amphibian embryo.

[0134] Embodiment 53. The multicellular construct of embodiment 51 or 52, wherein the ciliated epithelial cells are derived from Xenopus embryo.EXAMPLES

[0135] The following examples are illustrative and should not be interpreted to limit the scope of the claimed subject matter.

[0136] Example 1 - Methods to generate and characterize Neurobots

[0137] Disclosed herein are biological machines, neurobots, formed from skin and neural tissue extracted from frog embryos. The examples below demonstrate that neurobots are indeed viable and show a large degree of behavioral variability and increased complexity compared to their non-neuronal counterparts, xenobots. The neurons within a neurobot are active and send projections towards other constituent neuronal and non-neuronal cell types. Neurobots offer a unique opportunity to investigate the rules that govern the formation of neural networks within a novel environment, and to devise methods for harnessing the computational power of these networks to control and predict behavioral outcomes.

[0138] We are currently assessing the relationship between neural activity and movement in un-stimulated, naturally formed neurobots. This invention has potential for a wide range of applications in areas where controllable, biodegradable micro-robots are needed.

[0139] The following steps were taken to generate and characterize neurobots:

[0140] 1 The animal cap, i.e. a piece of ectoderm from the animal hemisphere of the late blastula or early gastrula stage Xenopus laevis embryos, was excised under a dissecting scope and the cells within the tissue were dissociated in a calcium and magnesium free solution (Figure 18A).

[0141] 2. At least 40-50 animal caps were excised and dissociated. The dissociated cells were allowed to rest for about 30 minutes. (FIG. 18A, B).

[0142] 3 A P200 (set at 80 uL) pipette was used to remove dissociated cells for transfer, being careful to minimize fluid uptake. Multiple in and out movements with the pipette were performed to remove the superficial pigmented ectoderm which does not dissociate well.

[0143] 4 Cells were transferred, 80 uL at a time, to a large (150x50 mm) agarose coated petri dish filled with 0.75X MMR (agarose made with 0.75X MMR), minimizing the amount of liquid transferred. The deeper dish format allows for higher dilution of the calcium magnesium solution whose presence has been found to be detrimental for the next reaggregation step (7).

[0144] 5 Once all the dissociated cells were transferred to the 0.75X dish, a P1000 pipette was used to distribute cells as evenly as possible in the dish, ensuring that cells were not sitting close to one another or bunched up near the boundaries of the dish.

[0145] 6. Cells were incubated for 3-4 hours. At this time, the dissociated cells assume neural fate (Grunz and Tacke 1989, Wilson and Edlund 2001, Kuroda et al 2005)

[0146] 7. Dishes were placed on a standard analog shaker (VWR) for 1 hour set on low speed (2) to bring the cells to the middle of the dish.

[0147] 8. Cells were then rested for another hour with the shaker turned off.

[0148] 9. The dish was carefully transferred under a stereoscope and gently rocked from side-to-side for a few minutes. This causes the formed aggregates to start getting larger, and also allows more aggregates to form.

[0149] 10. Forceps (Dumont -Dumoxel 03) were used to break apart the very large neural aggregates into smaller neural aggregates (neural clumps), sized to fit inside an animal cap.

[0150] 11 A P1000 pipette was used to take the amount of a neural aggregate needed for a neurobot (one large clump or a few smaller clumps). The neural aggregates were pipetted insideindividual wells of an agarose coated 6 well plate. Each well is be used for constructing one neurobot.

[0151] 12. After clumps enough for 6 neurobots were placed in the 6 wells, embryos from an afternoon fertilization (now in late blastula / early gastrula stage) were used to excise 6 animal caps as described previously.

[0152] 13. The caps were positioned with dorsal side down, one per well. Before the caps closed up, forceps were used to pick up the neural clumps and insert them inside the animal cap (Figure 24C, D).

[0153] 14. These constructs were allowed to rest for at least 30 minutes before moving the dish to the incubator (14°C).

[0154] 15. Media was changed daily over the next several days. The bots can be kept in tissue culture treated 6 well plates (no agarose coating is necessary).

[0155] 16. For calcium imaging experiments of freely moving bots using wide field fluorescence microscopy, neurobots were made using cells taken from embryos injected with GCaMP. For these experiments, in order to keep neurons in the focal plane, flattened neurobots were formed as follows.

[0156] 17. 24-48 hour after their initial construction (step 14), the bots were transferred into a 35x10mm Falcon Culture Dish and a Corning Glass cover slip with thickness of 1 1 and size of 22x22 mm was used to put pressure on the bots.

[0157] 18. First the dish was filled halfway with 0.75X MMR, up to 3 bots were placed in it, and the cover slip was picked up with a pair of tweezers and placed afloat on top of the bots.

[0158] 19. The coverslip was then gradually lowered by slowly draining MMR using aPl 000 pipet, squeezing the bots gradually. The coverslip was lowered as far as possible, but where the tissue remains intact.

[0159] 20. The bots were rested for 3 hours.

[0160] 21. The MMR was slowly pipetted back in, making sure the bots were not stuck on the cover glass before removing it from the dish.

[0161] 22. The hots, flattened as described above, are viable and move around in the dish.Flattening them also reduces the probability of them flipping and thus makes tracking the calcium signal of individual neurons easier.

[0162] 23. Within a week the implanted neurons grow and extend elaborate neurite processes towards various cell types on the interior and exterior (Figure 18E).

[0163] References

[0164] Grunz, H., and L. Tacke. 1989. “Neural Differentiation of Xenopus Laevis Ectoderm Takes Place after Disaggregation and Delayed Reaggregation without Inducer.” Cell Differentiation and Development: The Official Journal of the International Society of Developmental Biologists 28 (3): 211-17.

[0165] Hedrick, Tyson L. 2008. “Software Techniques for Two- and Three-Dimensional Kinematic Measurements of Biological and Biomimetic Systems.” Bioinspiration & Biomimetics 3 (3): 034001.

[0166] Kuroda, Hiroki, Luis Fuentealba, Atsushi Ikeda, Bruno Reversade, and E. M. De Robertis. 2005. “Default Neural Induction: Neuralization of Dissociated Xenopus Cells Is Mediated by Ras / MAPK Activation.” Genes & Development 19 (9): 1022-27.

[0167] Wilson, S. I., and T. Edlund. 2001. “Neural Induction: Toward a Unifying Mechanism.” Nature Neuroscience 4 Suppl (November): 1161-68.

[0168] Induces Nerve Growth and Promotes Visual Learning via Posterior Eye Grafts in a Vertebrate Model of Induced Sensory Plasticity.” NP J Regenerative Medicine 2 (March): 8.

[0169] Grunz, H., and L. Tacke. 1989. “Neural Differentiation of Xenopus Laevis Ectoderm Takes Place after Disaggregation and Delayed Reaggregation without Inducer.” Cell Differentiation and Development: The Official Journal of the International Society of Developmental Biologists 28 (3): 211-17.

[0170] Hedrick, Tyson L. 2008. “Software Techniques for Two- and Three-Dimensional Kinematic Measurements of Biological and Biomimetic Systems.” Bioinspiration & Biomimetics 3 (3): 034001.

[0171] Kolberg, Liis, Uku Raudvere, Ivan Kuzmin, Priit Adler, Jaak Vilo, and Hedi Peterson. 2023. “G:Profiler-Interoperable Web Service for Functional Enrichment Analysis and Gene Identifier Mapping (2023 Update).” Nucleic Acids Research 51 (Wl): W207-12.

[0172] Shimada, Tadayuki, and Kanato Yamagata. 2018. “Pentyl enetetrazole-Induced Kindling Mouse Model.” Journal of Visualized Experiments: JoVE, no. 136 (June). doi.org / 10.3791 / 56573.

[0173] Wang, Xiao, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017. “Community Preserving Network Embedding.” Proceedings of the AAAI Conference on Artificial Intelligence 31 (1). doi.org / 10.1609 / aaai.v31i 1.10488

[0174] Wilson, S. I., and T. Edlund. 2001. “Neural Induction: Toward a Unifying Mechanism.” Nature Neuroscience 4 Suppl (November): 1161-68.Example 2: Self-organizing neural networks in novel moving bodies: anatomical, behavioral, and transcriptional characterization of a living construct with a nervous system

[0175] Sensing cues from the environment and translating them into appropriate responses is the fundamental function of the nervous system in all animals. Critically, nervous systems endow animals with the ability to generate context, and experience-dependent changes in their behavior. A great deal is known about the development of biological neural networks and the neural code in animal models, which have arrived at their current structure-function relationship through evolution by natural selection. On the other hand, nervous systems are known to be plastic and adapt both structurally and functionally, on a much shorter timescale, to changes in sensory / motor effectors that might occur in the lifetime of an organism, e.g. as a result of injury, amputation or sensory deprivation, albeit in an age dependent manner1 4. The degree to which the nervous system of an animal can adapt to a new body plan, is especially impressive in cases where changes in sensory-motor architecture is particularly drastic. For example, ectopically induced eyes in the tail of Xenopus tadpoles, which were shown to confer vision to the host5.

[0176] What are the limits to neuroplasticity of nervous systems? Could we grow functional nervous systems even in completely novel embodiments? Creating truly novel configurations of biological material allows us to probe the plasticity of evolutionary hardware toadapt on developmental (not evolutionary) timescales to truly novel circumstances and has applications for regenerative medicine, human augmentation and biological engineering.

[0177] When ectodermal tissue is excised from the animal pole of a late blastula stage Xenopus embryo, and allowed to develop ex vivo, it will develop into a 3D “mucociliary organoid” and exhibits behaviors different from those observed in tadpoles of the same age6, expressing the four cell types normally present in a tadpole skin. These include multiciliated cells (MCCs), mucus secreting goblet cells, ionocytes that regulate ionic homeostasis of epidermis, and small secretory cells (SSCs)7’8. MCCs act as motor effectors in these organoids via flow arising from their polarized beating, generating a suite of stereotyped movement trajectories and velocities. Interestingly, SSCs were shown to secrete serotonin, which stimulates an increase in ciliary beating frequency through serotonergic receptors expressed on MCCs9. These self-motile mucociliary organoids, which we will refer to herein as biobots, are capable of navigating aqueous environments, generating a suite of stereotyped movement trajectories and velocities.

[0178] In this study we asked, what would happen if we provided these biobots with the ‘raw materials’ for building a nervous system. That is, if we used not only embryonic ectodermal cells, but also included neural precursor cells for building them. Would such neural precursor cells indeed differentiate into functional neurons within the biobot? How does the behavior and morphology of these ‘neurobots’ compare to non-neuronal counterpart?

[0179] Here we show that neural precursor cells harvested from Xenopus embryos and implanted in biobots made from Xenopus ectodermal cells indeed differentiate into functional neurons and extend their processes within and towards to outer surface of the neurobot. We show that neurobots show significant differences in their behavior and anatomy compared to their non- neuronal counterparts. Transcriptomic characterization of neurobots reveals significant upregulation of genes involved in nervous system development, compared to their non-neuronal biobots10 l3, including those important for processing light stimuli.

[0180] Results

[0181] Neurobots can be constructed by implantins exogenous neural precursors intoXenopus ectodermal explants.

[0182] To characterize the structure and function of a nervous system that self-assembles in a novel embodiment, we established an experimental procedure to implant biobots with neural precursor cells during the first few minutes of their formation. As shown previously, biobots can be constructed by excising tissue from the animal hemisphere of a Nieuwkoop and Faber stage 9 Xenopus laevis embryo (animal cap). In the course of 30 minutes, the excised tissue will gradually heal, initially forming a ‘bowl’ shape before forming a closed spherical shape6. We used the time window prior to the closure of the tissue, to insert neuronal precursor cells inside the healing tissue (Fig. lai, laii-top left panel).

[0183] In order to obtain neural cells, we took advantage of the fact that, if in the late blastula and early gastrula stage the animal cap is excised and dissociated and the dissociated cells are allowed to sit apart from each other for 3 hours or more, they will assume neural fate14 15. To obtain aggregates of neural precursors, we dissociated animal caps from ~50 embryos, let them sit apart for 3 hours, reaggregated them and then placed clumps of reaggregated cells inside a freshly excised animal cap before it fully healed into a spherical shape (Fig. lai, laii, top left panel, see Methods). Within 30 minutes the formed composite assumes a spherical shape, and by the second day, it is fully healed (Fig. laii, top right panel). Like non-neuronal biobots, by the third day, multiciliate cells start appearing on their outer surface and they starts moving around in the dish. Similar to biobots, neurobots have a lifespan of about 9-10 days without being fed, and can survive on consuming maternal yolk platelet present in all early Xenopus embryonic tissue6. Interestingly, however, by day 6, neurobots tend to have more elongated shape compared to the biobots and they become significantly larger (Fig. lb,c). In order to investigate whether the difference in size and elongation is simply due to implanting the animal caps with additional cells, we generated a third type of bot (sham neurobots) in a similar fashion as neurobots, except that the implanted cells were not allowed to sit apart for 3 hours and were instead reaggregated shortly after dissociation (within 30 minutes), largely preventing the induction of neural faith. We found that the sham neurobots were not elongated and did not show a significant size difference compared to biobots (Fig. 19 a,b). These results indicate that the elongation and increase in size is likely due to neuronal growth and within neurobots.

[0184] Implanted neural precursor cells differentiate into functional neurons making projections within the neurobot as well as towards the cells lining the outer surface.

[0185] To determine whether the implanted cells indeed have differentiated into neurons, we fixed and stained the neurobots with an antibody that specifically binds to acetylated alpha tubulin, which is abundantly present in neurons and multiciliated cells (see Methods). We found that the implanted neural precursor cells indeed differentiate into neurons. These neurons extend their processes not only within the neurobot, but also towards the outer surface (see arrows in Fig. 2c, d) Such projections towards the cells lining the surface of the bot suggests the possibility of neurons exerting control over surface effectors including multiciliated cells and / or those that modulate the ciliary beating frequency, e.g. small secretory cells. There was a large degree of variability in the structure of sprouting in different neurobots. No two neurobots showed the exact same neural architecture (Fig. 20). In a way this is not surprising given the variability in their initial conditions resulting from their manual construction and the variability of the amount of implanted tissue. This variability, however, allowed us to investigate correlations between various physical and behavioral characteristics of neurobots, which we will discuss in more detail in the following sections. Despite these differences, we found that in most neurobots, neural processes tended to emanate from one or more nuclei marked by high density of nuclear stains, presumably corresponding to the implanted clumps of cells (Fig. la Fig. 2c, d, Fig. 20). Interestingly, these regions were often surrounded by regions with seemingly no nuclear staining (Fig. 2b-d, Fig. 20). The presence of this ‘empty space’ is very intriguing and we hypothesize that the region might be comprised of neural support structures such as extracellular matrix. Support for this hypothesis includes cases where we observed neurites traversing ‘long distances’ in this empty space on a very straight line (Fig. 2b, yellow arrow). Additional support for this hypothesis comes from the significant upregulation in genes encoding various proteins associated with the ECM, which we will present later in this manuscript. Future experiments are needed to fully characterize the nature of this internal space.

[0186] In order to assess whether neurons within neurobots are indeed functional, we built neurobots using neural cells that were extracted from embryos with genetically encoded calcium indicators (GCaMP6s, see Methods), as this tool is commonly used to study neural activity16. Figure 3 shows an example of calcium signals measured from a freely moving neurobot, which was circling around in the dish (data not shown). Motion corrected videos (see Methods) were then analyzed to extract fluorescent activity in regions of interest. The implanted cells indeed showed calcium activity in all recorded neurobots suggesting that they are indeed capable of generatingsingle as well as bursts of spiking activity, as evident based on the duration of calcium activity (Figure 3). We also occasionally observed synchronized activity in nearby or distant regions of interest, suggesting neural connectivity (see e.g. arrowheads in Fig. 3c). Measuring neural signals in freely moving neurobots using widefield fluorescent imaging, however, proved to be challenging as it was not possible to keep individual regions of interest in focus over long periods of time. Moreover, since every neurobot showed a different pattern of neural expression (Fig. 20), findings in one neurobot could not be reproduced and confirmed in others. Developing methods for creating consistent neural expression and testing neurobots in experimental setups where a change in course of movement can be observed repeatedly (e.g. a maze) are critical for assessing presence of correlations between neural activity and behavior.

[0187] Neurobots tend to be more active and show an increase in their movement complexity compared to biobots

[0188] In order to assess behavioral phenotypes in neurobots and investigate potential differences with non-neuronal biobots, we video recorded spontaneous movements of neurobots and biobots as in small 8-well plates (n=46 neurobots and 48 biobots, Figure 4a, data not shown). We used an automatic tracking software17to measure the position of each bot at each video frame. Figure 4b shows the details of the 2D trajectories of the 8 neurobots depicted in panel a. There was a large degree of variability in these trajectories, with some bots moving in a circular / oval trajectories with relatively constant diameter (Fig. 4b, bots #3, #8), bots that followed circular traj ectories that were varied in diameter over time (Fig. 4b, bot #4), ones that made more complex, sometimes spirograph-like patterns (Fig. 4b, bots #1, #5, #6), those that were seemingly following the dish’s boundaries (Fig. 4b, bot #7), bots circled over very small areas (Fig. 4b, bot #2) and those that did not move at all (data not shown). Interestingly, all moving bots tended to exhibit repeating behavioral motifs.

[0189] Using the positional information obtained from tracking, we used a custom python code to extract 4 kinematic parameters from the bots to compare neurobots with biobots. These were total distance travelled in 30 minutes, speed, acceleration, and percentage of the well that was traversed (see Methods). We found no significant difference in the total distance traveled, the percentage of the well covered and average speed and acceleration. However, we found that theminimum movement speed of neurobots was significantly higher than that of biobots, indicating that neurobots were unlike biobots in that they tended to move more, as opposed to remaining idle.

[0190] To further investigate potential differences in the complexity of movement trajectories, we used a spectral analysis as follows: we first calculated the power spectral density (PSD) of the trajectory time series along the X and Y coordinates (Figure 5a-b. two exemplar trajectories and their corresponding time series on the X coordinate, see Methods). We then detected significant peaks in the X and Y PSD, summed the number of unique peaks in X and Y PSDs, and defined this number as the Complexity Index (Fig. 5c). In this analysis a complexity index of one indicates a circular trajectory with constant diameter (where both X and Y PSD have one peak at the same location), and the number increases as the trajectory becomes more complex. A complexity index of zero signifies non-moving bots.

[0191] Interestingly, we found that neurobots showed a significantly higher degree of trajectory complexity compared to biobots (Fig. 6a). This increased complexity could not be explained by the roundness or size of the biobots and neurobots as these variables were not significantly correlated (Fig. 6b, c, correlation coeeficient (Cl, Area)= -0.03, p=0.76 and correlation coefficient (CI, RI)=-0.15, p=0.2). The increased complexity relative to biobots could be a result of increased variability in the beating frequency of cilia in MCCs, changes in their spatial distribution, changes in the 3D structure of the bot, or changes in the activity or distribution of other cell types, including neurons, that may modulate the ciliary beating frequency, or other reasons. When we measured this index in sham neurobots, however, we also found an increase, but not significant, relative to biobots (Fig. 19c), indicating that the increased complexity we observe in neurobots is at least partially due to factors other than neural signaling. Similar to biobots, sham neurobots were more likely to show longer periods of immobility and their minimum speeds were not significantly different from that of biobots (Fig. 19d).

[0192] Finally, consistent with the finding that the minimum speed was significantly higher in neurobots (Fig. 6d), we found that the majority of inactive bots (Npeaks=0) were in the biobot category, with 6 out of 48 (12.5%) biobots inactive, whereas only one out of 47 (2.1%) neurobots were inactive.

[0193] Seizure-inducing drug differentially affects behavior of neurobots and biobots

[0194] In order to further investigate whether neural activity could play a role in modulating trajectory complexity, we performed pharmacological experiments treating groups of neurobots and biobots with pentylentetrazol (PTZ), which is a GABAA receptor antagonist and is used for its seizures-inducing effects in animal studies18. Although we do not know the identity of neuronal constituents of neurobots, we reasoned that a positive result, e.g. an increase in the complexity index after PTZ treatment, could be an indirect indication of presence of GABAergic control of movement. To test this, we performed experiments where we video recorded the movement of neurobots and biobots in regular media and compared the complexity indices after they were transferred to dishes containing 15 mM PTZ. To get an estimate on the baseline complexity index and account for potential variability due the transferring, we followed an experimental protocol depicted in Figure 8a.

[0195] Behavior of bots (16 neurobots and 16 biobots) was video recorded for 30 minutes in 8-well plates fdled with regular media (control 1), after which the bots were transferred to the second and third sets of dishes containing regular media (control 2, control 3). The bots were then transferred to dishes containing PTZ (Fig. 8a). For each bot, we calculated the Cis for the three control conditions and used the average to calculate relative complexity measures for PTZ and the wash. We found that all except two biobots showed a relative decline in their movement complexity while in PTZ (Fig. 8b), and thereby the CI was significantly reduced relative to control (T test, p=0.009). The majority of neurobots on the other hand, showed increased complexity relative to control, although a few did show a decline (Fig. 8b). As a result of this dichotomy in the impact of PTZ, the average CI in neurobots was not significantly different relative to control (T test, p= 0.39). When we compared the relative complexity after PTZ treatment, however, we found a significant difference between neurobots and biobots, with neurobots showing significantly higher values for relative complexity (Fig. 8c). The variability of the impact of PTZ on neurobots is in fact not surprising given the potential variability in the identity and the degree of expression of neurons (Fig. 7, Fig. 20). What is very interesting is the significant differential impact on the biobots and neurobots. Because neurobots are biobots + neurons, the fact that the majority of them showed an increase in their CI suggests a role for neural activity acting against the default inhibitory effect of PTZ on the movement of biobots. Alternatively, neural expression could indirectly contribute to these effects through its impact on the expression of MCCs or other cell types present on the outer surface of the bots (Fig.7, see also Discussion).

[0196] Neurobots show significant differences in the distribution of motor effectors and their roundness is anti-correlated with the decree of neural expression

[0197] In order to quantify the overall amount neural expression and its relationship with neurobot morphology and behavior, we used the confocal images of neurobots immunostained with acetylated alpha tubulin antibody, which labels neurons and cilia in the MCCs. Using this data, we traced the neural processes, and quantified the position of the MCCs using Imaris software (Fig. 7a, b, see Methods). In this analysis, we did not distinguish between axons or dendrites and could not determine whether these processes belonged to individual neurons. We could, however, obtain rough estimates on the total amount of neural tissue and the degree of branching by calculating the total length of neurites and the number of terminals, which were defined as the number of nerve endings, agnostic of their identity (i.e. axonal or dendritic). Figure 7a, b shows two examples of such traces in cases of neurobots with very few and many neurites. We calculated the correlation between the degree of sprouting, expression of MCCs, bot size and shape as well as the CI of the trajectories across all neurobots for which we had both behavioral and structural data (Fig.7c), We also calculated correlation of all these parameters with the relative amount of neural tissue that was implanted on Day 1 as the neurobots were constructed. This ratio was calculated by dividing the area of the implanted clumps to that of the external shell (Fig. laii, see Methods).

[0198] Based on this analysis we found that the total number of neuron terminals were highly correlated with both absolute and area-normalized neural length (neurite density). Interestingly, we found a significant negative correlation between neurite density and MCC expression density: neurobots with higher neurite density tended to have lower overall density of MCCs. Consistent with this finding, we found that biobots and sham neurobots (which do not have neurons) have a significantly higher density of MCCs compared to neurobots (Fig 21). Additionally, we found a significant negative correlation between the Roundness Index (RI) and neurites’ absolute length, neurites’ normalized length, and total number of terminals. That is, the more elongated the bot, the more neural expression, suggesting that the elongation could be a result of neural processes growing within the neurobot. This hypothesis is consistent with the finding that sham neurobots were not different than the biobots in their roundness index (Fig. 19a). We did not however, find a significant correlation between CI and neural expression although theneurobot with highest complexity index also had the largest number of terminals and neurite length (see the outlier on the panels). Similarly, we only found a small correlation (non-significant) between the relative amount of implanted tissue and the degree of neural expression.

[0199] Previous studies had shown that treatment with zolmitriptan, which is a selective 5-hydroxytryptamine (5-HT) 1B / 1D receptor agonist, increased the degree of ectopic (but not native) neural sprouting in Xenopus embryos19. We investigated whether this treatment would have an impact on the degree of sprouting in neurobots, where in fact they are all ‘ectopic’. Interestingly, we found that this treatment increased the degree of neural expression in neurobots as well, although it did not have a significant effect on any of the behavioral measurements including trajectory complexity (Fig. 22). In this group we found a tight correlation between the ratio of neural implant to ectoderm shell and total number of terminals as well as total neural length (Fig 22). These results indicate that neurons in a neurobot behave as ectopic, not native, cells.

[0200] Notably, 4 the 16 neurobots in the PTZ study presented in the previous section, were cultured in zolmitriptan (filled circles Fig. 8b, Fig. 22), three of which showed an increase in relative complexity. This result points to the possibility that this treatment may bias neural expression towards those that respond to PTZ i.e. GABAergic neurons. Further experiments are required to characterize the impact of zolmitriptan treatment on the neural expression patterns within neurobots.

[0201] In summary, neural growth in neurobots significantly impacts their shape and the distribution of MCCs, and this growth could be potentially increased by modulating serotonergic signaling. The degree to which neural expression contributes to trajectory complexity remains elusive. The lack of observed correlation between neurite growth and complexity index points to the potential heterogeneities in cell type expression and connectivity profiles of neurites across neurobots.

[0202] The three types ofbots exhibit a significantly different pattern of gene expressions

[0203] Like CNS structure and function, transcriptomes are usually thought of as being shaped by a long history of selection. In standard organisms, they are also shaped by neural inputs20,21. What would the transcriptome of a novel construct with a nervous system look like?Thus, we next asked what changes to default biobot transcriptomes, if any, would be induced by the presence of neural tissue. To characterize the transcriptome of neurobots and compare it to its non-neuronal conouterparts (i.e. biobots and sham neurobots), we performed bulk RNA sequencing of their tissue. For each bot type four biological samples were included (NB1-4, BB1- 4, SHI -4). Due to the small size of the bots, and therefore, small amount of RNA, each sample comprised of tissue from multiple bots (see Methods).

[0204] We found a high degree of correlation between normalized gene expression levels among all samples within each group (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, FPKM22) , which indicates reliability and repeatability of the results (Fig. 9a). Moreover, we found that gene expression levels in biobots and sham neurobots were much more correlated to one another than to neurobots (Fig. 9a, see Methods). Similarly, neurobots could clearly be separated from shams and biobots based on the principal component analysis on the normalized gene expression value (FPKM) of all samples (Fig. 9b, see Methods).

[0205] We next compared the gene count variability across the samples of biobots, neurobots, and sham neurobot son a gene-by-gene basis (Fig. 9c-f). We found that neurobots showed a significantly higher variability, quantified by coefficient of variation (C V, see Methods) in their normalized gene counts (FPKM) across neurobot samples, compared to both biobots and shams, and samples of sham neurobots showed a higher variability compared to samples of biobots (Fig. 9c). We then compared pairs of groups (e.g., NB and BB) to determine the fraction of genes that had a greater CV in normalized gene count in one group than the same gene in the other group. For the chosen pair of groups, genes were ranked by the mean count value across all pools of both groups, and the CV of each gene’s counts across the pools of each group was calculated. The CV list was split into 100 bins (percentiles) containing equal numbers of genes, and the fraction of genes in the bin for which the CV of the first group was greater than that of the second group was found and plotted.

[0206] We found that in all bins, more than half of the NB genes showed higher CV compared to BB and SH group (Fig. 9d,f, blue line is at 0.5). For the SH group genes in most, but not all bins showed higher CV compared to BBs (Fig. 9c). In addition, we found a trend for genes with higher normalized counts showing higher degree of variability in their expression whencomparing NBs with BBs and SHs (Fig. 9d,f). This pattern was significantly different from what is expected from chance in most bins (dark blue bins), i.e. relative to the average CV calculated across all bins regardless of the order (orange line, see Methods). The overall higher variability seen in neurobots and shams compared to biobots could be due to several factors. First, in both neurobots and shams, the implanted cells are harvested from ~50 embryos whereas a biobot is made out of a single embryo. Moreover, higher variability in the implanted bots is expected due to the high variability in the size of the implants in both neurobots and shams. However, these are likely not the only factors involved, as neurobots showed significant differences in their gene count variability compared to shams. Neural differentiation, therefore, likely plays an important role in the increased variability in gene counts seen in neurobots.

[0207] Additionally, we found that neurobots included a significantly larger number of genes that were differentially expressed relative to biobots and sham neurobots (Fig. 10 a,b), whereas biobots and sham neurobots exhibited a smaller subset of differentially expressed genes (Fig. 10c) Moreover, the number of significantly upregulated genes (red dots with positive log fold change) in neurobots compared to biobots and shams, were much higher than those that were significantly downregulated (red dots with negative log fold change), resulting in highly asymmetric volcano plots (Fig. 10 a,b). This was not the case when comparing shams with biobots (Fig. 10c). These results are consistent with a gain of function as a result of neural growth in neurobots.

[0208] We next investigated which biological functions or pathways are significantly associated with the differentially expressed genes. We used Gene Ontology (GO) enrichment analysis, which annotated genes to biological processes (bp), molecular function (mf) and cellular components (cc). Due to the large number of upregulated genes in neurobots, we focused on the highly overexpressed genes (4 log-fold or more increase in expression) for this analysis. The most significantly upregulated pathways in neurobots relative to biobots as well as in neurobots relative to shams related to nervous system development, synapse and neuron projection (Fig. 11 a,b). Trans-synaptic signaling and neurotransmitter receptor activity were also significantly upregulated in neurobots. This included glutamatergic, GABAergic, cholinergic, dopaminergic, serotonergic, and glycinergic receptors. Surprisingly, we additionally found significant enrichment in pathways involved in visual perception in neurobots (Fig. Ila).

[0209] Although pathways relating to neuron projection and trans-synaptic signaling were slightly upregulated in sham neurobots compared to biobots, there were far less number of genes in each pathway and they were less significant in their degree of upregulation compared to those in neurobots (Fig. 24a). Further, there were relatively fewer genes downregulated when comparing neurobots with biobots andno genes were significantly downregulated by 4- log folds when comparing neurobots with shams or sham neurobots with biobots. The largest group of downregulated genes in neurobots compared to biobots belonged to the cellular component pathway; extracellular region (Fig. 24b). Interestingly, this pathway includes some of the genes that are expressed in the Xenopus skin, including glycoprotein 2 (gp2), and mucin (rm clT) suggesting that the properties of the ‘skin of neurobots’ might be different from those of biobots.

[0210] In order to identify functional biological modules of differentially expressed genes, we extracted the largest protein-protein-interaction (PPI) sub-network of these genes using the STRING database. We then performed network embedding and clustering using multi- nonnegative matrix factorization (MNMF)23to find specific functional biological modules. The clusters were subsequently enriched using g:Profiler24. Based on this analysis, we identified 25 clusters for neurobots vs biobots comparison and 5 clusters in neurobots vs sham neurobots comparison (Table 1, Fig 25 and 26). There were not enough upregulated genes between biobots and sham neurobots to allow for this analysis. Similarly, due to the low number of downregulated genes, the network analysis could not be performed neither at 4-, nor at 2-fold change threshold level.

[0211] Consistent with the findings from the enrichment analysis, we found clusters that contained genes critical for the development of the nervous system including wnt signaling pathways and Spemann organizer formation (Cluster 3, Fig. 25, Table 1-NBvsBB), and genes encoding different aspects of the nervous system (e.g. within Clusters 5, 9,11,14,15 (Fig. 12a- b), 18,19, 21, 23 (Fig. 12c-d), 25, Fig. 25, Table 1-NBvsBB). This included genes critical for formation of axons, synaptic vesicles, pre and postsynaptic structure, ion channels and various neurotransmitters and their receptors.

[0212] Notably, one of the largest clusters (Cluster 1) contained genes encoding various aspects of visual perception (Fig 12 e-f, Table 1-NBvsBB). Specifically, this cluster includedvarious members of opsin family, retinal G-protein couple receptors (rgr), melanopsin (opn4, opn5), rhodopsin (rho), as well as many other related genes that encode proteins involved in visual processing (Fig. 12f).

[0213] Moreover, we found that there was a significant upregulation in genes encoding extracellular matrix constituents (Fig. Ila, Fig. 25, Cluster 20, Table 1- NB vs BB) including collagen (col 17a, co!4a2), which is the most abundant fibrous protein and constitutes the main structural element of extracellular matrix25, and fibrillins, which are glycoproteins that are secreted in the extracellular matrix and provide mechanical support in connective tissue (fblnl)26. This finding provides support for that the internal cavity of neurobots may be indeed not be empty, but comprise of ECM materials, providing structural support for neural processes.

[0214] As expected, network embedding and clustering analysis of the upregulated genes in neurobots relative to shams similarly revealed overexpression of genes relating to synapse organization, regulation of neurotransmitter and receptor activity and chemical synaptic transmission (within Clusters 4,5, Fig. 12 g,h, Fig 26, Table 1), visual perception (within Cluster 3, Fig 26, Table 1-NBvsSH), neuron projection and perineuronal nets (within cluster 2, Fig 26, Table 1-NBvsSH).

[0215] Specifically, Cluster 5 shows a highly significant upregulation of genes related to synapse organization and regulation of neurotransmitter activity. Some of the key genes in this cluster included those encoding AMPA receptors (grial), Activity-regulated cytoskeleton- associated protein (Arc), brain-derived neurotrophic factor (bdnf), and calcium / calmodulin dependent protein kinase II alpha (CamKIIa) (Fig. 12h), which are additionally known to be important for synaptic plasticity and learning20.

[0216] Additionally, we found a significant upregulation in genes that regulate sleep (e g. hypocretin neuropeptide precursor, hcrf), as well as those that regulate responses to external stimuli (within Cluster 1, Fig 26, Table 1-NB vs SH). The latter included genes involved in wound healing (angpt2), those important for controlling development, survival and maintenance of microglia (csflr).

[0217] These findings shed light on biological pathways / molecular functions that are innately present in neurobots in the absence of any external manipulations and will inform future work towards building ‘specialized’ neurobots through selective enhancement of these pathways.

[0218] We applied a phylostratigraphic analysis for the differentially expressed genes in the different conditions (Fig. 13, NB vs SH and NB vs BB). Interestingly, we found that more than 54% of the upregulated genes in neurobots fall into the two categories of most ancient genes (“All living organisms” and “Eukaryota”, Fig. 13a). In addition, in comparison, very few ancient genes are downregulated. In total 279 are downregulated in these two groups for the NB vs BB conditions and 233 for the NB vs SH condition (Fig. 13b) while for the upregulated genes we obtained 941 and 1109, respectively. Therefore, we conclude that while becoming neurobots, there is a transcriptomic shift towards very ancient genes for neurobots compared to biobots and shams.

[0219] Table 1. 25 clusters for neurobots vs biobots comparison

[0220] Discussion

[0221] In this study we built and investigated behavioral, anatomical and transcriptional properties of novel living constructs with incorporated neural tissue. Using Xenopus laevis embryonic cells, we built two types of living constructs: ones built using ectodermal cells (biobots) as reported in prior studies6’27’28, and novel constructs made using ectodermal and neural precursor cells (neurobots, Fig. la). We show that neurobots are viable and self-motile like their nonneuronal counterpart (data not shown), and that the implanted neural precursor cells indeed differentiate into neurons and extend their processes throughout the construct (Fig. 1-2, Fig. 20).

[0222] We found that neurobots became significantly larger than biobots and they were significantly more elongated (Fig. lb,c). We saw a large degree of variation in the shape and innervation pattern of neurobots, but in all neurobots, we found a central ‘cavity’ seemingly devoid of any cell bodies (Fig. 2, Fig. 20). We speculate that this region is not truly empty and may be filled with extracellular matrix-like structures (ECM). The presence of neurites extending in extremely straight course points to presence of such supporting structure (Fig. 2b, red arrowhead). Consistently, our transcriptomics data showed significant upregulation in the expression of genes related to ECM in neurobots (Fig. lla,b, Fig. 13). This finding points to tight coupling between neural network formation and ECM gene expression, even in the context of a completely novel embodiments.

[0223] Neurobots exhibited a diverse range of movement patterns, and these patterns were more complex than those observed in their non-neuronal counterparts, indicating that neural expression may affects movement either directly, i.e. through neural signaling to the motor effectors, or via changes in the expression patterns of motor effectors. Indeed, we did observed a negative correlation between the degree of neural expression and the density of multiciliated cells (Fig. 7C).

[0224] Simultaneous recording of neural activity and behavior could be used to assess the potential neural correlates of the behavior. Our calcium imaging experiments indicated presence of neural activity, however, measuring this activity in freely moving neurobots was complicated by their movement, which often resulted in losing track of specific neurons. Especially neurobots which showed a more complex movement pattern, often moved in 3D, making it even more tricky to follow individual neurons’ activity overtime. Such 3D movements could be avoided by making flattened neurobots, however, these neurobots tended to not move as much (data not shown). The role of neural activity in trajectory complexity may be alternatively investigated in future through silencing / activating neurons, either pharmacologically or using optogenetics, and measuring the impact on trajectory complexity. Consistent with a role of neural activity in trajectory complexity, we found that treatment of biobots and neurobots with the GABAA receptor antagonist PTZ resulted in significantly different outcomes. Although most biobots decreased their movement complexity with PTZ treatment, this was not the case for neurobots (Fig. 8). In fact, the majority of neurobots showed an increase in their movement complexity, suggesting a role for neural activity to work against the action of PTZ on the non-neuronal biobots. Further experiments are required to assess neural control of spontaneous, as well as evoked behaviors.

[0225] Anatomically, we found that although the majority of neural processes emanating from the implanted neural precursor ‘clumps’ sprouted within the central cavity of the bot, some processes did extend towards the outer epithelium (arrows heads Fig. 2, Fig. 20). Although we have not demonstrated presence of synaptic connectivity with the surface epithelial cells, these neural processes are in a position to allow them to modulate activity of the surface epithelial cells, including multiciliated cells, goblet cells, and serotonergic cells among others.

[0226] We raised a small group of neurobots in zolmitriptan, which is a selective 5- hydroxytryptamine (5-HT) 1B / 1D receptor agonist, known to increase the degree of ectopic neuralsprouting in Xenopus embryos19. Interestingly, three out of four of these neurobots showed an increase in movement complexity when treated with PTZ (filled circles Fig. 8b, Fig. 22). Moreover, these neurobots, showed a tighter correlation between the amount of implanted tissue and degree of innervation (Fig. 22).

[0227] Our transcriptomics analysis revealed the landscape of differentially expressed genes between neurobots, biobots and sham neurobots. Overall, neurobots showed a significant upregulation in gene expression compared to biobots and sham neurobots whereas biobots and shams were more similar to one another (Fig. 9a, b, Fig. 10). Our functional enrichment analysis revealed that neurobots, compared to both biobots and shams, exhibit high level of enrichment in genes involved in nervous system development, synapse, neuron projection and trans- synaptic signaling (Fig. lla,b). Genes encoding major neurotransmitter receptors were present in the transcriptome of neurobots. This included glutamatergic, GABAergic, cholinergic, dopaminergic, serotonergic, and glycinergic receptors.

[0228] Our gene network analysis resulted in identification of multiple functional clusters, which allowed us to more deeply examine the genes and pathways that are upregulated in neurobots. Notably, we found a large cluster containing genes with important roles in visual perception (Cluster 1, Fig. 13e,f). This cluster included genes that are normally exclusively expressed in the Xenopus eyes and included various members of opsin family, such as retinal G- protein coupled receptor, various cone opsins, and rhodopsin, as well as genes encoding many other proteins that are implicated in visual processing. This remarkable finding suggests the possibility of presence of visually evoked behaviors in neurobots. The most exciting next step will be to test this hypothesis and discover the ways light could modulate motor output in neurobots. If present, this will be a completely novel emergent behavior. Finally, based on a phylostratigraphic analysis, we show that the majority of upregulated genes in neurobots compared to biobots and shams, consist of the most ancient genes (Fig. 13).

[0229] In summary, our results show that it is possible to build synthetic novel constructs that incorporate neural tissue. Populations of neurons within neurobots exhibit significant diversity in both their architecture and identity and show propensity for innervating cells that line the outer surface of the construct, which include motor effectors and modulators. Although we have not shown direct evidence on neural modulation of neurobot behaviors, our pharmacological studiessuggest that this could indeed be a possibility. This study sheds light on potential ways by which build biological robots that be built to perform specific tasks using neural control and contributes to our understanding of the plasticity of evolutionary hardware to adapt on developmental (not evolutionary) timescales.

[0230] Methods

[0231] Construction of biobots, neurobots and shams

[0232] Biobots were constructed as described previously6by excising tissue from the animal hemisphere of a Nieuwkoop and Faber stage 9 Xenopus laevis embryo (animal cap). To construct neurobots and sham neurobots we excised 40-50 such animal and let them sit with external surface facing up in 60 mm petri dishes filled with a calcium magnesium free solution (50.3 mM NaCl, 0.7 mM KC1, 9.2 mM Na2HPO4, 0.9 mM KH2PO4, 2.4 mM NaHCO3, 1.0 mM edetic acid (EDTA), pH 7.3), and coated with 1% agarose made in the same solution. After about -30-40 min the cells were fully dissociated. The dissociated cells were transferred to a deep 60 mm petri dish containing 0.75X MMR, using a P200 pipette, taking as little liquid as possible. For constructing sham neurobots, the cells were immediately reaggregated and formed into clumps (see below). For constructing neurobots we dispersed the dissociated cells as far as possible by moving the solution in the dish sing a P1000 pipette. The cells were left still in the dish for -3-4 hours. To reaggregate cells, the dish containing dissociated cells was placed on a shaker and cells were thereby brought together to the middle of the dish. They were then allowed to reaggregate for -1 hour. At this time, various ‘clumps’ of cells were formed. Using a P1000 pipette, clumps of - >10 cells were moved into the wells of an agarose coated 6 well plate, containing 0.75X agarose. Two or three cell clumps were next used for implanting depending on the size of clumps. Next, animal caps were dissociated from a second batch of embryos from a later fertilization (at late blastula, early gastrula stage), and the animal caps were placed with external surface facing down individually in the wells of the same 6 well plate. The excised animal cap slowly forms a cup and eventually ‘closes up’ within 10-15 min. Clumps of neural precursor cells or (non-neuronal clumps in the case of shams) were placed inside the cup before it closed using fine forceps, and enough time was allowed for the animal cap to fully close before moving the dish to the incubator.

[0233] Immunohistochemistry

[0234] Bots were fixed overnight at 4 degrees Celsius in 4% paraformaldehyde (manufact.) with 0.25% Gluteraldehyde (manufact) individually in 96 well plates. The next day, they were washed three times at room temperature in PBT (0.1% Triton X-100 Manufact in PBS- / - Manufact) for at least 15 minutes and then incubated in the 10% Casblock (Manufact) in PBT for at least 1 hour. They were then transferred into the solution containing primary antibody (Anti- Acetylated Tubulin antibody, Mouse monoclonal, Sigma T7451) and Hoescht (33342, Thermo Scientific). The plate was sealed using parafilm and covered in foil for light protection and placed on a shaker in the cold room for 3 days at 4C. The bots were next washed 3 times in PBT at room temperature and then transferred and incubated overnight at 4C in the secondary antibody (goat anti-Mouse Alexa 594, Manufact), with the dish sealed with parafilm and covered in foil. Finally, the bots were washed again 3 times in PBT and either stored in PBS in 4C or mounted into 15 m- slide 18 well flat dishes (Ibidi 81821) in an antifade mounting medium (Vectashield) for confocal imaging.

[0235] Calcium imaging

[0236] Embryos at four cell stage were microinjected in all four blastomeres with mRNA encoding the genetically encoded fluorescent calcium indicator GaCaMP6s. These embryos were used for obtaining clumps of neural precursor cells for implantation. Albino embryos were used as the outer shell of the neurobots in these experiments so that the fluorescent signals from neurons could be visualized more easily as wild type embryos are pigmented. We used a custom-built microscope to measure calcium activity in freely moving neurobots (data not shown). We used Fiji’s32Descriptor Based Series Registration plugin to correct for the motion of the neurobot (data not shown), and then used Suit2p software33to identify active units (Fig. 3). In order to avoid movements in Z-direction, which resulted in changes in plane of focus, we created ‘flattened’ neurobots as follow: on the next day after their formation neurobots were pressed down using a glass coverslip which was gradually lowered over them as small amounts of MMR were removed from the dish. Neurobots were left under pressure for 3 hours, after which MMR was gradually added to the dish resulting in the release of the coverslip.

[0237] Quantification of the bot shape and neural tracing

[0238] We used the brush tool in Fiji32to fill in shape of the bot and calculated the area and the roundness index defined as the major axis / minor axis. We used the same tool to estimatethe relative amount of implanted neural precursor tissue by dividing the are of the implanted clumps to the outer shell (Fig. 1 a). We used Imaris software (Oxford Instruments) to quantify neural expression using confocal stacks acquired from the bots that were stained with antibodies against acetylated alpha tubulin, which labeled neurons and cilia in multiciliated cells. We manually traced neurites using the filament function and exported values corresponding to the total length of neurites (dendrite length sum parameter in Imaris) and the number of terminal points (number of dendrite terminal points parameter in Imaris). For the analysis of the multicilited cell distribution, we estimated the total number of MCCs by marking the center of each MCC using the Spots tool in Imaris and calculated the total number of multiciliated cells. We then used this value to calculate the MCC density by dividing this number to the total area of the bot.

[0239] Behavioral analysis

[0240] Videos of bot movements were taken over 30 minutes under various conditions and tracked with the DLTdv digitizing tool17in MATLAB and the x and y coordinates of the center of mass were calculated. A custom Python code was used to extract various kinematic variables using the time series of the coordinates. We calculate total Euclidean distance travelled, speed and acceleration. Additionally, we calculated the percentage of the well that was traversed by the bots by dividing the space of each well into 0.1 mm bins. We then calculated the parameter: ‘percent covered area’ by dividing the number of unique visited bins by the total number of bins. We calculated a complexity index by first calculating the power spectral density (PSD) of the trajectory time series along the X and Y coordinates and identifying significant peaks in the power. We calculated Welch’s power spectral density estimate with a window size of 400s and overlap of 1 s between windows for each of the x and y time series. We picked a threshold of 10 pixels2 / Hz (-0.17 mm2 / Hz= 0.4 mm / Hz) to detect peaks in the PSD of the x and y coordinates. This threshold was chosen to remove the baseline noise corresponding to tracking of the center of mass of bots that had an average radius of 0.4 mm. We then defined the complexity index as the total number of unique peaks in x and y PSDs.

[0241] RNA-sequencing and bioinformatics

[0242] We submitted 12 samples (4 samples per biobot type, 5-15 biobots per sample) submerged in Trizol (Invitrogen) in 2 mb Eppendorf tubes to Novogene for low-input, high lipid, bulk RNA extraction. Equal quantities of RNA were sequenced from each sample usingNovaSeq6000 sequencer, resulting in consistent library size across samples. Clean reads were extracted from FASTQ fdes, removing reads with adapter contamination, when uncertain nucleotides constitute more than 10 percent of either read (N > 10%), and when low quality nucleotides (Base Quality less than 5) constitute more than 50 percent of the read. The index to the reference genome (Xenopus laevis version 10.1) was built using Hisat2 v2.0.534and clean reads were aligned to the reference. The mapped reads of each sample were assembled using StringTie (vl.3.3b)33and FeatureCounts vl.5.0-p336was used to count the reads numbers mapped to each gene. FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis was performed using the DESeq2 R package (1.20.0)37and the resulting p-values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate.

[0243] The webapp g:Profiler24was used to perform functional enrichment analysis of differentially expressed genes. For each comparison, upregulated genes (p-adjusted < 0.05; log2foldchange > 4) and downregulated genes (p-adjusted < 0.05; log2foldchange < 4) were separately mapped from Xenopus to human symbols using the HGNC Comparison of Orthology Predictions (HCOP) tool38. Genes lacking an established gene symbol were removed from analysis. Each gene list was separately queried using g:Profder across all data sources. The statistical data scope included only annotated genes and the g:SCS method was used for computing multiple testing corrections for p-values at a threshold of p<0.05. The R package ggplot2 (v3.5.1)39was used to generate dot plots of gene ontology driver terms from g:Profiler. Driver terms were determined by grouping significant terms into sub-ontologies based on their relations and then identifying the leading gene sets that give rise to other significant functions in the ontology neighborhood.

[0244] For network analysis and clustering we applied network analysis techniques to discover biological functional modules40,41. By integrating gene expression and interaction data, we extracted protein-protein interactions (PPI) for the different biobots in the different conditions and applied network embedding and clustering techniques as described similarly to Cantini et al. (2015)42and Pio-Lopez et al. (2021)43. Specifically, we used the MNMF algorithm developed by Wang et al. (2017)23for network embedding and clustering. To create a network for the biobots, we started by isolating genes of interest and identifying corresponding human orthologs under various conditions using the HCOP database44We then used the STRING database45to extractrelevant PPT networks. The clusters identified through our network embedding and clustering method were further analyzed for enrichment using g: Profiler24.

[0245] Analysis of gene expression variability

[0246] The normalized gene count variability was compared between groups (ACs, NBs, and SHs) using a MATLAB script using the method summarized in Fig. 23. For each gene in each pair of groups being compared, the mean count value across all pools of both groups was found, and genes were ranked from greatest to least mean. Genes for which any of the counts across all pools was 0 were discarded. Because the count value of a given gene in a given pool represents the mean value of all the individuals in that pool, the standard deviation of the pools gives the standard error of the means (SE) of the group. The SE is related to the number of individuals per pool (n) and the standard deviation of the individuals within the pools (o) with the equation SE = <j / sqrt(n). By multiplying the SE by sqrt(n), o can be calculated. Dividing a by the mean count value of the pools gives the coefficient of variation (CV). Genes with equivalent CVs in both groups were discarded. The ranked gene CV lists were then split into 100 bins (percentiles) containing equal numbers of genes from highest to lowest counts. Within each bin, the number of genes with greater C V for the first group than the second were counted and divided by the bin size to find the fraction of genes in the bin with greater CV in the first group. These fractions were then plotted as bar graphs in Figure 1 C-E, with a blue line marking 0.5. The bin values appeared to vary with gene count percentile, so to determine the statistical significance of each bin’s departure from its expected value, a permutation test was used. For each plot (each pair of groups), the order of the gene pairs was randomly shuffled (keeping pairs together), and new bins were generated. This was repeated 1,000 times for different random shuffles to produce a distribution of bin fraction values for each bin. The p-value of each bin was defined as the proportion of the bin fractions from the distribution that were further in absolute value from the distribution mean than the true bin fraction. Bins with p-values of p<0.05 were deemed statistically significant and were colored dark blue. All other bins were colored light blue. The mean bin value from the distributions was plotted as a yellow line.

[0247] Phylostratigraphic analysis

[0248] We employed the phylostratR package (Arendsi et al., 2019) to conduct a phylostratigraphic analysis on neurobot transcripts, with Xenopus laevis (taxon ID ‘8355’)designated as the reference species. This software automates several key steps in evolutionary analysis: (1) it builds a clade tree using species from the UniProt database and aligns it with the latest NCBI taxonomy; (2) the clade tree is trimmed to maintain a phylogenetically diverse selection of representative species for each phylostratum; (3) a comprehensive protein sequence database is constructed from hundreds of species based on this clade tree, with additional data such as human and yeast proteomes manually added, resulting in 329 species for our study; (4) a similarity search is performed by conducting pairwise BLAST comparisons between the proteins encoded by Xenopus laevis and those of the target species; (5) the 'best hits' are identified, and gene homology is inferred between Xenopus laevis and the target species; (6) each gene is assigned to a phylostratum that corresponds to the oldest clade for which a homolog is identified. Genes specific to Xenopus laevis are classified as orphan genes and placed within the Xenopus laevis' phylostratum. The evolutionary stages we focused on include: All living organisms (bacteria, eubacteria), Eukaryota, Opisthokonta, Metazoa, Eumetazoa, Bilateria, Deuterostomia, Chordata, Vertebrata, Gnathostomata, Euteleostomi, Sarcopterygii, Tetrapoda, Anura, Xenopus, and Xenopus laevis.

[0249] This methodological approach enables a detailed examination of gene emergence and their evolutionary trajectories across various taxa. By implementing Phylostratr, we systematically mapped the age of the neurobot genes in the different conditions with a specific phylostrata to understand the distribution of ages of the neurobots overexpressed genes. We used the upregulated and downregulated genes in neurobots (logFC>4 and logFC<-2 respectively).

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[0251] In the foregoing description, it will be readily apparent to one skilled in the art that varying substitutions and modifications may be made to the invention disclosed herein without departing from the scope and spirit of the invention. The invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention. Thus, it should be understood that although the present invention has been illustrated by specific embodiments and optional features, modification and / or variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[0252] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0253] Citations to a number of patent and non-patent references are made herein. The cited references are incorporated by reference herein in their entireties. In the event that there is an inconsistency between a definition of a term in the specification as compared to a definition of the term in a cited reference, the term should be interpreted based on the definition in the specification.

Claims

CLAIMSWe Claim:

1. An engineered multicellular organism comprising an aggregate of ciliated cells and neuronal cells, wherein the organism moves when the ciliated cells are actuated.

2. The organism of claim 1, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

3. The organism of claim 1, wherein the organism comprises a sensor for detecting a target molecule.

4. The organism of claim 1, wherein the cells of the organism self-assemble.

5. The organism of claim 1, wherein the organism has an effective diameter of about 100-500 microns.

6. The organism of claim 1, wherein the organism moves at a rate of at least about 15 microns / second when the ciliated cells are actuated.

7. The organism of claim 1, wherein the neuronal tissue comprise neurites, optionally, wherein the neurites extend to the ciliated cells.

8. The organism of claim 1, wherein the ciliated cells are eukaryotic epidermal cells.

9. The organism of claim 1, wherein the ciliated cells, the neuronal cells, or both are engineered to express a heterologous molecule.

10. The organism of claim 9, wherein the heterologous molecule is a reporter molecule11. The organism of claim 9, wherein the heterologous molecule is an enzyme that metabolizes a target substrate.

12. The organism of claim 9, wherein the heterologous molecule is a receptor for a target ligand.

13. The organism of claim 1, wherein the aggregate of cells is response to light.

14. The organism of claim 1, wherein the organism is configured for moving a target object.

15. The organism of claim 1, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

16. The organism of claim 1, wherein the organism comprises amphibian cells.

17. A plurality of the organism of claim 1, wherein the plurality exhibits collective and / or coordinated behavior.

18. The plurality of claim 17, wherein the collective and / or coordinated behavior is collective and / or coordinated movement.

19. A method for removing a target substrate from an environment, the method comprising engineering the organism of claim 1 or a plurality thereof to express an enzyme that metabolizes the target substrate and placing the organism in the environment.

20. A method for detecting a target ligand in a sample, the method comprising engineering the organism of any of claim 1 or a plurality thereof to express a receptor for the target ligand and place the organism in the sample, wherein the organism generates a signal after the receptor binds the target ligand.

21. A method for detecting movement of an organism of claim 1 or a plurality thereof, wherein the organism or the plurality thereof expresses a photoconvertible fluorescent reporter molecule, and the organism generates a fluorescent signal when the photoconvertible fluorescent reporter molecule is exposed to light.

22. A method for preparing the engineered multicellular organism of claim 1 or a plurality thereof, the method comprising explanting cells from tissue and culturing the explanted cells under conditions in which the cultured, explanted cells form the engineered multicellular organism or the plurality thereof.

23. A system comprising an engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, the system comprising a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when the ciliated cells are actuated and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form a multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated.

24. The system of claim 23, wherein the engineered multicellular organism is semitoroidal in shape.

25. The system of claim 23, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

26. The system of claim 23, wherein the cells of the organism self-assemble.

27. The system of claim 23, wherein the organism has an effective diameter of about 100-500 microns.

28. The system of claim 23, wherein the organism moves at a rate of at least about 15, 20, 25, 50, 100, 200, 500, or 1000 microns / second when the ciliated cells are actuated.

29. The system of claim 23, wherein the neuronal cells comprise neurites that contact the ciliated cells.

30. The system of claim 23, wherein the ciliated cells are eukaryotic epidermal cells.

31. The system of claim 23, wherein the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object.

32. The system of claim 23, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

33. The system of claim 23, wherein the organism comprises amphibian cells.

34. A method for forming an engineered multicellular organism comprising an aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated, the method comprising combining in cell media a first engineered multicellular organism comprising an aggregate of ciliated cells and an aggregate of neuronal cells, the system comprising a plurality of dissociated cells, wherein the engineered multicellular organism is capable of moving when theciliated cells are actuated, optionally wherein the neuronal cells direct or influence the actuation of the ciliated cells, and the engineered multicellular organism moves the plurality of dissociated cells into piles of cells which form the multicellular organism comprising the aggregate of ciliated cells which is capable of moving when the ciliated cells are actuated.

35. The method of claim 34, wherein the engineered multicellular organism is semitoroidal in shape.

36. The method of claim 34, wherein the organism consists of biological material and / or does not comprise any inorganic material, for example as a scaffold.

37. The method of claim 34, wherein the cells of the organism self-assemble.

38. The method of claim 34, wherein the organism has an effective diameter of about 100-500 microns.

39. The method of claim 34, wherein the organism moves at a rate of at least about 15, 20, 25, 50, 100, 200, 500, or 1000 microns / second when the ciliated cells are actuated.

40. The method of claim 34, wherein the organism does not comprise neural cells or neural tissue.

41. The method of claim 34, wherein the ciliated cells are eukaryotic epidermal cells.

42. The method of claim 34, wherein the organism is configured for moving a target object, optionally wherein the organism comprises a hole or cavity for holding the target object.

43. The method of claim 34, wherein the organism is configured to have a cavity for capturing and / or transporting a target object.

44. The method of claim 34, wherein the organism comprises amphibian cells.

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