Use of engineered multicellular organisms for screening antiaging and pro-regenerative compositions

Abstract

Disclosed are methods of using multicellular constructs in methods to detect anti-aging or pro-regenerative compositions. The disclosed organisms comprise an aggregate of ciliated cells, and the organisms move when the ciliated cells are actuated. Also disclosed are populations of motile multicellular constructs with improved longevity, methods of generating motile multicellular constructs with improved longevity, and methods of reducing the cellular age of a sample of ciliated epithelial cells.

Description

Atty. Dkt. No. 166118.01552USE OF ENGINEERED MULTICELLULAR ORGANISMS FOR SCREENING ANTIAGING AND PRO-REGENERATIVE COMPOSITIONSCROSS-REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This application claims priority benefit from U.S. Application Serial No. 63 / 680,033 filed August 6, 2024 and U.S. Application Serial No. 63 / 797,614 filed April 30, 2025. The entirety of each of which is incorporated herein by reference.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under grant HR0011-18-2-0022 and W911NF1920027 both awarded by the Department of Defense. The government has certain rights in the invention.SEQUENCE LISTING

[0003] A sequence listing (file name: 166118_01552.xml; size: 1,864 bytes; date generated: July 29, 2025) is hereby incorporated by reference in its entirety.FIELD

[0004] The field of the invention relates to engineered multicellular organisms and systems and methods for designing, preparing, and utilizing engineered multicellular organisms as a model for aging and degeneration.BACKGROUND

[0005] Biobots have significant potential to impact discovery in biology and biomedical engineering, emerging as a unique blend of biological and robotic concepts. Biobots can be categorized into three types: non-cellular biobots, hybrid biobots, and fully-cellular biobots [1], Non-cellular biobots are entirely robotic but inspired by biological forms or behaviors. Examples include Wehner et al. fully autonomous soft “octobot” [2], Ze et al.’s origami millibots [3], Dillinger et al.’s micron-scale ciliary biobots [4], Miskin et al.’s electro-microscopic robots [5], and several others [6] that can be controlled via magnetic, ultrasonic or electrochemical actuation. A prime feature of non-cellular biobots is their reductionist design approach, which provides full control over structure and behavior. Hybrid biobots, a combination of biological and inanimate components [7], are exemplified by Nawroth et al.’s 2012 tissue-engineered jellyfish [8] as wellAtty. Dkt. No. 166118.01552 as Park et al.’s 2016 biohybrid ray [9], These and other following examples of biobots

[0010] integrate specialized tissues (e.g., muscle) with inert scaffolds (e.g., PDMS gels), leveraging this synergy to drive form and function. In contrast, fully cellular biobots stand out due to their lack of inanimate material shaping and supporting the structure and resulting function, instead relying solely on the inherent capacity of biological cells to autonomously form new forms and functions, such as self-motility. The first examples of these fully-cellular biobots were Kriegman & Blackiston et al.’s different types of Xenobots

[0011] , ciliated spheroids derived from frog embryos. These bots were followed by Gumuskaya et al.’s first fully cellular human-derived non-embryonic biobots in 2023, dubbed “Anthrobots” [1],SUMMARY

[0006] In an aspect of the current disclosure, methods of detecting a composition that is effective for reducing a sign or symptom of aging are provided. In some embodiments, the methods comprise: (a) contacting a test population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition; and (b) detecting a longevity parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0007] In an aspect of the current disclosure, methods of detecting a pro-regenerative compound are provided. In some embodiments, the methods comprise: (a) contacting a test population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition; (b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0008] In some embodiments, the methods comprise (a) contacting a test population of mechanically damaged multicellular constructs with a test composition and contacting a control population of mechanically damaged multicellular constructs with a control composition; (b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0009] In some embodiments, the methods comprise (a) mechanically damaging a plurality of multicellular constructs; (b) contacting a test population of the mechanically damaged multicellular constructs with a test composition and contacting a control population of theAtty. Dkt. No. 166118.01552 mechanically damaged multicellular constructs with a control composition; (c) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0010] In an aspect of the current disclosure, populations of multicellular constructs with improved longevity are provided. In some embodiments, the multicellular constructs comprise ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct, wherein the mean initial cross-sectional area of population of multicellular constructs is greater than about 30,000 square microns (sq. microns), greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range.

[0011] In an aspect of the current disclosure, methods of generating a population of multicellular constructs with improved longevity are provided. In some embodiments, the methods comprise separating, partitioning, enriching, or isolating a population of multicellular constructs with a mean initial cross-sectional area of greater than about 30,000 sq. microns, greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range, from an initial population of multicellular constructs to generate a population of multicellular constructs with improved longevity, wherein the multicellular constructs comprise ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct.

[0012] In an aspect of the current disclosure, methods of reducing the cellular age of a sample of ciliated epithelial cells are provided. In some embodiments, the methods comprise causing an apical-basal inversion of the ciliated epithelial cells in vitro to reduce the cellular age of the ciliated epithelial cells.BRIEF DESCRIPTION OF THE FIGURES

[0013] FIGs. 1A, IB, and 1C show that Anthrobots have different modes of selfconstruction that may account for the emergent morphotypes. Three fates of an AnthrobotAtty. Dkt. No. 166118.01552 progenitor cell: dormant, merger expander, and monoclonal expander. (FIG. 1 A) PCA cloud consisting of 28 Anthrobots’ growth time course biomass data, evaluated with both morphological and temporal indices. The three orthogonal clusters identified by the unsupervised clustering algorithm are labeled as groups 1, 2, and 3, indicating three major fates respectively: dormant, merger expander, and monoclonal expander. The indices forming the PCA dimensions are as follows. The “merge events” index is the master metric that distinguishes between Anthrobot formation via monoclonal expansion versus cluster merging: if a spheroid is undergoing monoclonal expansion, this number would be 1, while if the final spheroid is a result of multiple spheroids merging together, this number would denote the number of times a merge event has occurred. The “fin area” measures the area of the final spheroid, while “sd area” quantifies the average standard deviation of area across entire growth process. (FIG. IB) These three modes of growth are further described with the associated boxplots. (FIG. 1C) Representative examples from the PCA, as labeled on the cluster plot, (cl) An example dormant bot. Notice volume stays the same. Scalebar lOum. (c2) An example of hybrid Anthrobot formation through a merge event. Notice the heterogeneous structure in comparison to cl and c3. Scalebar lOum. (c3) An example monoclonally expanding, imaged bidaily. Scalebar 50um.

[0014] FIGs. 2A, 2B, 2C, 2D, 2E, and 2F show that early-stage Anthrobots display onset markers of embryonic development. As NBHEs progress from progenitor cells to Anthrobots, we observe gene patterning characteristics of mammalian germ layer development and axis formation. (FIG. 2A) Example morphologies of “progenitor cell,” at the beginning of Anthrobot growth time course, and “day 0 bot,” meaning it's on the last day of bot formation time course and day 0 of its spheroid (i.e., bot state) time course. (FIG. 2B) PCA of clustering of Anthrobots across different life stages. Progenitor and day 0 bot stages are shown with dashed circles. (FIG. 2C) The difference in gene expression between the progenitor vs day 0 stage, showing significant differences in the transcriptome, with notable genes explained further in FIG. 2D. (FIG. 2D) Functional genes for germ layer formation (FOXA1,FOXA2FOXC1, BMP7, and FOXP2) as well as axis formation (BMP7, HESX, and SHH) were observed to display expression profiles of early embryonic development. (FIG. 2E) Histogram of the phylostratigraphic analysis of the Anthrobots and progenitor cells in the different conditions, showing the number of genes expressed with logCPM>l in the different conditions. (FIG. 2F) Number of overexpressed DEGs with logFC>2 in the different conditions. For FIG. 2E) and FIG. 2F), the X-axis shows theAtty. Dkt. No. 166118.01552 evolutionary ages of ancient genes (here ‘All living organisms’ and ‘Eukaryota’, the former corresponds to Eubacteria, bacteria and their descendants). The Y-axis shows the gene counts.

[0015] FIGs. 3A, 3B, 3C, 3D, and 3E show that Anthrobots display different behaviors during the eversion process. (FIG. 3A) Sample Anthrobot eversion time course. Day 0 Anthrobot going through polarity reversal via eversion across 48 hours. (FIG. 3B) PCA cloud consisting of 30 Anthrobots’ eversion time course biomass data, evaluated with both morphological and behavioral indices. The two orthogonal clusters identified by the unsupervised clustering algorithm are labeled as groups 1 and 2, indicating two major modalities of eversion. Plotting high-dimensional cloud 2D. (FIG. 3C) These two modes of eversion are further described with the associated boxplots. More specifically, while a majority of Anthrobots stay in place during eversion (group2), which would be the expected behavior, a smaller subset of bots (about one third) seems to be displacing during polarity reversal (groupl) as shown on graph i (p=0.00027). This motility is likely caused by disorganized forces generated by radical anatomical activity, causing large variability in the heading of the displacing bots as shown in graph ii (p=0.00018). The fact that there is no difference in terms of the metrics that measure organized behavior such as gyration (graph iii) or straightness (graph iv) between these two modes of eversion further supports this explanation. Straightness P=0.051, Gyration P= 0.8. (FIG. 3D) Representative examples from each group across time. The individual bots are marked on the PCA cloud in FIG. 3B as botDl and botD2. (FIG. 3E) Collection behavior demonstrated by one of the motile eversion bots (group2). The residual filament in cell culture media (shown with an arrow) is collected by a group 2 bot that ultimately encases the filament debris during eversion.

[0016] FIGs. 4A, 4B, 4C, and 4D show that late-stage Anthrobots display markers of maturing embryonic development. As NBHEs progress from apical-in state on day 0 to apical- out state on day 10, we continue to observe gene patterning characteristics of mammalian germ layer development and axis formation. Colors green, blue, red and yellow respectively represent basal cells, nuclei, tight junctions, and cilia. (FIG. 4A) Example morphologies of “day 0 bot,” at the beginning of Anthrobot eversion time course, and “day 10 bot,” meaning it's at a mature motile state. (FIG. 4B) PCA of clustering of Anthrobots’ transcriptome across different life stages. Day 0 and day 10 bots are shown with dashed circles. (FIG. 4C) The difference in gene expression between the day 0 vs day 10 stage, showing significant differences in the transcriptome, with notable genes explained further in FIG. 4D. (FIG. 4D) FOXA1 and FOXA2, key transcriptionAtty. Dkt. No. 166118.01552 factors for lung epithelial cell proliferation and differentiation in endoderm, show no difference of expression in day 0 vs day 10 bots. Similarly, genes like FOXCI and BMP7 that are associated with mesoderm formation show decrease in expression. Conversely, an increase in the gene FOXP2, ectoderm marker, is seen as upregulated. Furthermore, for axis formation, while the dorsal -ventral patterning marker BMP7 gets downregulated in this later developmental stage, the anterior-posterior marker HESX stays steady and the previously silent left-right patterning marker SSH becomes upregulated on day 10.

[0017] FIGs. 5A, 5B, 5C, and 5D show that Anthrobots display robust behavioral longevity and reversal of epigenetic age. (FIG. 5A) Weekly counts of baseline behavioral activity (defined as any cilia-induced movement, regardless of the amount displaced) stay steady despite the shrinking population due to aging. (FIG. 5B) Towards the end of an Anthrobot lifecycle, between weeks 4-8, while there is visible degradation of bot structural coherence (see FIGs. 6A- 6C), the persistence of baseline behavior stays at a steady frequency. Population-level baseline behavior outlives individual bots. (FIG. 5C) Cells harvested from a 21-year-old donor were differentiated into Anthrobots, which were then collected at day- 10 and day-25 timepoints. Methylation clock studies revealed passage 1 primary cells’ tissue age as 25 (mean value) years old. Both against this baseline and against the recorded age of the donor, differentiation into Anthrobots introduced significant methylation clock reversal, with day 10 bots being read at the mean value of 18.7 years old, while the day 25 bots being read at the mean age of 20. Contrarily, an air-liquid-interface differentiation of the same cells into 2D airway tissue (ALI tissue) significantly increased tissue age to a mean of 25 years old. (FIG. 5D) Day 4 bot’s healing process from a hypodermic needle injury.

[0018] FIGs. 6A, 6B, and 6C show that regardless of initial size, Anthrobots degrade at a steady rate, resulting in large bots living longer. (FIG. 6A) PCA analysis showing 36 bots’ cluster analysis based on morphological indices characterizing the change in size across a 30-day timeframe. (FIG. 6B) Bots that start with a (i) large initial area (group 2) compared to bots that start with a smaller initial area (group 1) show the same (ii) relative percent change across their degradation time-course, maintaining the size difference (iii) at the end of the degradation process. Regardless of initial morphology, all Anthrobots are subject to a similar rate of aging, resulting in large bots living longer than smaller bots. Panels i and iii use arbitrary units in terms of pixel square. (Fig. 6C) Bot cl from group 1 (marked on the PCA) has a smaller initial area compared toAtty. Dkt. No. 166118.01552 bot c2 from group 2 with, a larger initial area. While hot 1 completely degrades within the same time frame, bot 2 still stays intact, suggesting a positive correlation between hots’ initial morphology and their life span as opposed to a finite time span across all population. Scalebar 50 sq. microns.DETAILED DESCRIPTION

[0019] Disclosed herein are methods of detecting a composition that is effective for reducing a sign or symptom of aging, methods of detecting a pro-regenerative compound, populations of motile multicellular constructs with improved longevity, methods of generating the same, and methods of reducing the cellular age of a sample of ciliated epithelial cells.Methods of detecting a composition that is effective for reducing a sign or symptom of aging

[0020] The inventors investigated the aging and degradation of the multicellular constructs described herein (also referred to herein as “Anthrobots” when derived from human cells), finding that regardless of their initial size, the multicellular constructs degrade at a steady rate, resulting in larger constructs (“bots”) living longer. Thus, the multicellular constructs provide an attractive platform for screening anti-aging candidate interventions, because they can be made patientspecific, and because unlike traditional cell culture and organoid models, they offer motility, which can be used to quantify effects of putative pro-longevity reagents not only on anatomical and molecular markers, but on active behavior. Accordingly, in an aspect of the current disclosure, methods of detecting a composition that is effective for reducing a sign or symptom of aging are disclosed.

[0021] In some embodiments, the methods comprise (a) contacting a test population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition; and (b) detecting a longevity parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0022] As used herein, a “test composition” may comprise any candidate anti-aging composition, including, but not limited to, a small molecule pharmaceutical, a biologic, a polynucleotide, an extracellular vesicle, a nanoparticle, a cell, a cellular construct, or any combination of the foregoing.Atty. Dkt. No. 166118.01552

[0023] As used herein, a “control composition” refers to a composition that is used as a direct control to the test composition and may comprise, e.g., vehicle used to deliver the test composition, e.g., phosphate buffered saline (PBS), cell culture media, DMSO, etc., and does not comprise the test composition.

[0024] A “population of multicellular constructs” refers to 2 or more multicellular constructs, e.g., 2-100, 2-1,000, 2-10,000, 2-100,000, 2-1,000,000, or more multicellular constructs, or at least 2 multicellular constructs.

[0025] A “test” population of multicellular constructs refers to a population of multicellular constructs, as described above, that is isolated to be contacted with a test composition. In contrast, a “control” population of multicellular constructs refers to a population of multicellular constructs, as described above, that is isolated to be contacted with a control composition. The test and control populations of multicellular constructs may be derived from a single population of multicellular constructs and separated from each other for the purpose of contacting the test population only with the test composition and for the purpose of contacting the control population only with the control composition.

[0026] In the present disclosure, the inventors further characterized Arthrobots which are an exemplary form of multicellular constructs derived from human airway epithelial cells. As used herein, multicellular constructs may be derived from ciliated epithelial cells, e.g., including, but not limited to, mammalian ciliated epithelial cells, primate ciliated epithelial cells, ape ciliated epithelial cells, or human ciliated epithelial cells. Exemplary ciliated epithelial cells include airway epithelial cells.

[0027] Exemplary longevity parameters include, but are not limited to, an average (mean, median, or mode) life span of the multicellular constructs, methylation clock analysis to determine an “epigenetic age” of the multicellular constructs (which may be presented as an average), and an average time to disintegrate.

[0028] The inventors demonstrated that the multicellular constructs have a finite life span and continue to be motile until they spontaneously disintegrate. Therefore, the skilled person may calculate the average life span of the multicellular constructs by monitoring their status, e.g., whether or not they have disintegrated, by using time-lapse microscopy, which may be performed as described in Example 1.Atty. Dkt. No. 166118.01552

[0029] Methylation clock analysis refers to analyzing the epigenetic age of the multicellular constructs, e.g., according to the following references a) S. Horvath, Genome Biol 2013, 14 (10), R115; b) S. Horvath, J. Oshima, G. M. Martin, A. T. Lu, A. Quach, H. Cohen, S. Felton, M. Matsuyama, D. Lowe, S. Kabacik, J. G. Wilson, A. P. Reiner, A. Maierhofer, J. Flunkert, A. Aviv, L. Hou, A. A. Baccarelli, Y. Li, J. D. Stewart, E. A. Whitsei, L. Ferrucci, S. Matsuyama, K. Raj, Aging (Albany NY) 2018, 10 (7), 1758; and c) S. Horvath, A. T. Lu, H. Cohen, K. Raj, Aging (Albany NY) 2019, 11 (10), 3238, which are each incorporated by reference herein in their entireties.

[0030] As used herein “time to disintegrate” refers to the number of frames until the multicellular construct becomes untrackable, e.g., through time-lapse microscopy and analyzed, e.g., with the program Fiji, and the AnalyzeParticles tool, which are publicly available.

[0031] The methods may further comprise administering the test composition to a subject, e.g., administering a therapeutically effective amount of the test composition to the subject. The test composition may be administered for the purpose of reducing a sign or symptom of aging in the subject. A “therapeutically effective amount” and appropriate route of administration may be determined by an attending physician according to established pharmacological principles. Further, an “therapeutically effective amount,” as used herein, refers to an amount of the test composition that achieves at least one therapeutic goal in the subject, e.g., reducing a sign or symptom of aging in the subject.

[0032] The disclosed methods may be performed using multicellular constructs derived from the cells of a subject, e.g., human airway epithelial cells may be taken from a subject and multicellular constructs (Anthrobots) may be generated and used in the disclosed methods. The test composition may be administered to the subject as described above. The subject may be suffering from a disease or disorder, e.g., a disease or disorder related to aging or premature aging, e.g., progeria. The subject may be a vertebrate, e.g., a mammal, e.g., a human, a non-human primate, a dog, a cat, a horse, a cow, a donkey, etc.Methods of detecting a pro-regenerative compound

[0033] In an aspect of the current disclosure, methods of detecting a pro-regenerative compound are provided. In some embodiments, the methods comprise (a) contacting a testAtty. Dkt. No. 166118.01552 population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition; and (b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0034] The multicellular constructs in the test population and in the control population may be mechanically damaged before step (a) of the method. For example, the multicellular constructs may be contacted with a solid structure, e.g., a needle, e.g., a hypodermic needle, as in Example 1. In other examples, the multicellular constructs may be chemically damaged, optically damaged, or electromagnetically damaged.

[0035] In some embodiments, the methods comprise (a) contacting a test population of mechanically damaged multicellular constructs with a test composition and contacting a control population of mechanically damaged multicellular constructs with a control composition; and (b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0036] In some embodiments, the method comprise (a) mechanically damaging a plurality of multicellular constructs; (b) contacting a test population of the mechanically damaged multicellular constructs with a test composition and contacting a control population of the mechanically damaged multicellular constructs with a control composition; and (c) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

[0037] Exemplary regeneration parameters include, but are not limited to, an average (mean, median, or mode) life span of the multicellular constructs, methylation clock analysis to determine an “epigenetic age” of the multicellular constructs (which may be presented as an average), and an average time to disintegrate.

[0038] The methods may further comprise administering the test composition to a subject, e.g., administering a therapeutically effective amount of the test composition to the subject. The test composition may be administered for the purpose of reducing a sign or symptom of degeneration in the subject.Atty. Dkt. No. 166118.01552

[0039] The disclosed methods of detecting a pro-regenerative composition may be performed using multicellular constructs derived from the cells of a subject, e.g., human airway epithelial cells may be taken from a subject and multicellular constructs (Anthrobots) may be generated and used in the disclosed methods. The test composition may be administered to the subject as described above. The subject may be suffering from a degenerative disease or disorder.Populations of motile multicellular constructs with improved longevity

[0040] With reference to FIGs. 6A-6C, the inventors demonstrated that, surprisingly, (i) Anthrobots that start with a large initial cross-sectional area (group 2) compared to bots that start with a smaller initial area (group 1) show the same (ii) relative percent change across their degradation time-course, maintaining the size difference (iii) at the end of the degradation process. Regardless of initial morphology, all Anthrobots are subject to a similar rate of aging, resulting in large bots living longer than smaller bots.

[0041] Accordingly, provided herein are populations of multicellular constructs with improved longevity. In some embodiments, the population comprises multicellular constructs comprising ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct, wherein the mean initial cross-sectional area of population of multicellular constructs is greater than about 30,000 sq. microns, greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range. The cross-sectional area is greater than about 2500 sq. microns to about 100,000 sq. microns, or any subrange or value therein.

[0042] The multicellular constructs may be Arthrobots which are an exemplary form of multicellular constructs derived from human airway epithelial cells or the constructs may be derived from ciliated epithelial cells, e.g., including, but not limited to, mammalian ciliated epithelial cells, primate ciliated epithelial cells, ape ciliated epithelial cells, or human ciliated epithelial cells. Exemplary ciliated epithelial cells include airway epithelial cells.Methods of generating populations of motile multicellular constructs with improved longevityAtty. Dkt. No. 166118.01552

[0043] Provided herein are methods of generating a population of multicellular constructs with improved longevity. In some embodiments, the methods comprise separating, partitioning, enriching, or isolating a population of multicellular constructs with a mean initial area of greater than about 30,000 sq. microns, greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range, from an initial population of multicellular constructs to generate a population of multicellular constructs with improved longevity, wherein the multicellular constructs comprise ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct. The cross-sectional area is greater than about 2500 sq. microns to about 100,000 sq. microns, or any subrange or value therein.

[0044] The multicellular constructs may be Arthrobots which are an exemplary form of multicellular constructs derived from human airway epithelial cells or the multicellular constructs may be derived from ciliated epithelial cells, e.g., including, but not limited to, mammalian ciliated epithelial cells, primate ciliated epithelial cells, ape ciliated epithelial cells, or human ciliated epithelial cells. Exemplary ciliated epithelial cells include airway epithelial cells.Methods of reducing the cellular age of a sample of cells

[0045] Provided herein are methods of reducing the cellular age of a sample of cells, e.g., ciliated epithelial cells. In some embodiments, the methods comprise causing an apical-basal inversion of the cells, e.g., ciliated epithelial cells in vitro to reduce the cellular age of the cells.

[0046] Methods of causing apical-basal inversion are known in the art and may include, but are not limited to removing the epithelial cells from a medium comprising extracellular matrix, e.g., placing the cells in medium that does not comprise extracellular matrix.

[0047] The extracellular matrix may be a basement membrane extract, e.g., Matrigel®.

[0048] 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,Atty. Dkt. No. 166118.01552 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.Additional Definitions and Terminology

[0049] The disclosed subject matter 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.

[0050] 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.”

[0051] 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.

[0052] 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.Atty. Dkt. No. 166118.01552

[0053] 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.

[0054] 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 further understood 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.”

[0055] 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.

[0056] 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.”EXAMPLES

[0057] The following examples are illustrative and should not be interpreted to limit the scope of the claimed subject matter.Atty. Dkt. No. 166118.01552Example 1 - The morphological, behavioral, and transcriptomic life cycle of Anthrobots

[0058] Reference is made to Gizem Gumuskaya et al. “The Morphological, Behavioral, and Transcriptomic Life Cycle of Anthrobots” Adv Sci (Weinh). 2025 Jun 6:e2409330. doi: 10.1002 / advs.202409330, the content of which is incorporated by reference herein in its entirety.

[0059] Introduction

[0060] Biobots have significant potential to impact discovery in biology and biomedical engineering, emerging as a unique blend of biological and robotic concepts. Biobots can be categorized into three types: non-cellular biobots, hybrid biobots, and fully-cellular biobots [1], Non-cellular biobots are entirely robotic but inspired by biological forms or behaviors. Examples include Wehner et al. fully autonomous soft “octobot” [2], Ze et al.’s origami millibots [3], Dillinger et al.’s micron-scale ciliary biobots [4], Miskin et al.’s electro-microscopic robots [5], and several others [6] that can be controlled via magnetic, ultrasonic or electrochemical actuation. A prime feature of non-cellular biobots is their reductionist design approach, which provides full control over structure and behavior. Hybrid biobots, a combination of biological and inanimate components [7], are exemplified by Nawroth et al.’s 2012 tissue-engineered jellyfish [8] as well as Park et al.’s 2016 biohybrid ray [9], These and other following examples of biobots

[0010] integrate specialized tissues (e.g., muscle) with inert scaffolds (e.g., PDMS gels), leveraging this synergy to drive form and function. In contrast, fully cellular biobots stand out due to their lack of inanimate material shaping and supporting the structure and resulting function, instead relying solely on the inherent capacity of biological cells to autonomously form new forms and functions, such as self-motility. The first examples of these fully-cellular biobots were Kriegman & Blackiston et al.’s different types of Xenobots

[0011] , ciliated spheroids derived from frog embryos. These bots were followed by Gumuskaya et al.’s first fully cellular human-derived non-embryonic biobots in 2023, dubbed “Anthrobots” [1],

[0061] Anthrobots are self-constructing fully-cellular biobots derived from human tracheal epithelia by reprogramming the development of an airway organoid. Airway organoids are three- dimensional structures grown in vitro that mimic the architecture and function of the human airway

[0012] , These organoids can derive from multiple sources, including stem cells, normal human bronchial epithelial cells (NHBEs), and human donors. Airway organoids are commonly characterized by their ability to replicate the cellular diversity of the airway epithelium, includingAtty. Dkt. No. 166118.01552 ciliated cells, goblet cells, and basal cells. They exhibit key functional properties of the airway, such as mucus production, ciliary beating, and response to external stimuli, making them accurate models for studying respiratory physiology and pathophysiology — or, in our case, ciliated synthetic biological constructs. The main difference between Anthrobots and airway organoid is that while the primary goal of the organoids is to recapitulate the native tissue architecture and physiology, Anthrobots and other biobots demonstrate new living form and function that is not explored by evolution but is useful for engineering purposes. They are used not only to make useful synthetic living machines but also to explore motility-enabled behavior that reveals plasticity and emergent features not apparent in organoid assays.

[0062] During maturation, various environmental cues can guide airway organoids towards certain morphologies, with apical-in or apical-out determination being one of the recently studied. By maintaining cell aggregates in growth media and Matrigel, airway organoids will develop a membrane with its apical surface facing inward. However, an apical-out phenotype has recently been shown, resulting in cilia covering the exterior of the organoids. This phenotype has been accomplished using a variety of techniques, including air-liquid interfaces with cells embedded in collagen

[0013] , microwells that mold individual cell aggregates

[0014] , U-bottom wells without any extracellular matrix present

[0015] , as well as in the case of Anthrobots, by way of sudden environmental phase shift from a semi-solid to a liquid state [1], All these different approaches can trigger bronchial epithelium samples from humans to spontaneously form ciliated spheroids with self-motility.

[0063] Anthrobots acquire shapes and behaviors quite different from any stage of human development or organogenesis; thus, they represent a fascinating opportunity to study the form and function of a new living configuration that had never been the subject of evolutionary selection in this form factor. Learning to predict and exploit emergent functionality of cellular materials is of critical importance to developing rational control in bioengineering and regenerative medicine contexts; Anthrobots are a new model system in which our nascent ability to predict novel life functions can be improved. Such synthetic examples of life are also interesting model systems from the perspective of evolutionary developmental biology and exobiology, as they are a unique context in which to understand “life as it could be”, as they differ from any known naturally- evolved organism. Lastly, because their morphogenesis and behavior are emergent in the context of wild-type human cells, and not under control of exogenous synthetic circuits, molecularAtty. Dkt. No. 166118.01552 reprogramming factors, or nanomaterials, they can teach us much about the plasticity of the molecular hardware encoded by the genome.

[0064] Past work [1] characterized Anthrobots’ shape, motility, and most strikingly, ability to induce repair in neural wounds in vitro. However, many aspects of their life history remained unknown, including the details of their formation process, the dynamics of their maturation and death, and their ability to respond to mechanical damage. While the bots come from adult (including elderly) donors, we investigated the age of the bots, and whether the process of morphogenesis could reverse the clock. Epigenetic clock analysis

[0016] revealed a significant reduction of age in the bots compared to their cellular source.

[0065] We also sought to characterize their transcriptome. While others study airway organoids as a model of human in vivo biology, we pursued a complementary perspective; we considered what genes a new type of creature navigating an environment for which it was not directly selected would express. We found massive alterations in the transcriptome of Anthrobots compared to their cells of origin, notably the up-regulation of evolutionarily ancient genes. This reveals unexpected ways in which emergent morphogenesis regulates gene expression, including the appearance of embryonic patterning genes despite the adult origin of the cells and lack of genomic editing or transgenes. Taken together, these data shed light onto the natural history, at the molecular, cellular, tissue, and behavioral levels, of this novel synthetic architecture.

[0066] Results

[0067] Anthrobots have three different developmental fates.

[0068] We first wanted to learn more about Anthrobots' self-construction process and specifically investigated whether this process unfolds through a single path or whether there are different ways for Anthrobots to self-construct as embedded in Matrigel®, which may shine light onto their emergent morphological and behavioral differences [1] . To this end, we collected microscopy images of 28 Anthrobots every 2 days to track their growth in Matrigel® throughout a 14-day developmental process. We analyzed these time-course data by through three major metrics: number of cellular collisions (“merge_events”), final spheroid area (“fm_area”), and the average standard deviation of area across the entire growth process (“sd_area”) as shown on FIG. 1A. When this three-dimensional data cloud was clustered with the WardD2 unsupervised clustering algorithm, we observed the emergence of three statistically orthogonal (FIG. IB)Atty. Dkt. No. 166118.01552 groups, each representing an Anthrobot growth type, which we named as dormant, merger expander, and monoclonal expander. The dormant typology consists of progenitor cells that start out as individual cells and never proliferate and develop into a multicellular spheroid (FIG. 1C-1). The merger expanders consist of the spheroids that result from the merging of one or multiple spheroids, each expanding from single cells through cellular proliferation (FIG. 1C-2). Finally, the monoclonal expanders are multicellular spheroids that can be tracked to a single ancestral cell (FIG. 1C-3). These classes are statistically distinct (as determined by the unsupervised WARD.d2 algorithm, p<0.01). Thus, we conclude that Anthrobots have three different developmental fates.

[0069] Anthrobots exhibit a highly altered, more ancient, transcriptome including embryonic patterning genes

[0070] Anthrobots undergo morphogenesis into a unique functional form despite their completely wild-type human genome. We investigated what genes Anthrobots express and how they compare to those of their parent tissue (airway epithelium) or to human embryos. To understand the plasticity of cell groups in the absence of genomic editing, we next sought to characterize their transcriptome. We first performed RNAseq on progenitor cells and day 0 Anthrobots (FIG. 2A), and computationally identified differentially-expressed genes (DEGs) that were unique to Anthrobots. Biological replicates of the samples clustered tightly, but showed very clear separation between the source cells and Anthrobots (FIG. 2B). Remarkably, plotting the transcripts with a strict cutoff (log2-fold change>4) for significance revealed massive remodeling of the transcriptome (FIG. 2C). Out of 22,518 transcripts, 8,992 showed significant up- or downregulation. Thus, becoming an Anthrobot has a major effect on the expressed genetic information.

[0071] Anthrobots self-assemble, but they are made of adult, not embryonic, cells. We considered whether the process involves any transcriptional programs normally driving embryonic development. We applied pathway analysis through the Gene Ontology (GO) database

[0017] and found that the “embryonic patterning” gene category was highly represented when Anthrobot DEGs were compared to transcriptomic data of a human morula. We found that 13.4% of the overexpressed genes in DayO bots compared to progenitor cells are genes activated at morula stage. In addition, we found an enrichment in ‘multicellular organism development’ (p<0.005). We conclude that the formation of Anthrobots involves the activation of some embryonic transcriptional programs.Atty. Dkt. No. 166118.01552

[0072] While Anthrobots appear to be spherically symmetric, and their source cells originate from the endodermal lineage, the embryonic-like transcriptional signature led us to ask whether they expressed any embryonic markers of axial patterning

[0018] and germ layer formation

[0019] (FIG. 2D). We observed the differential expression of both endoderm-driver genes like FOXA1 and FOXA2

[0020] as well as mesoderm drivers such as FOXCI

[0021] and BMP7

[0022] in the earlier phase of the developmental process from progenitor cells to a three-dimensional Anthrobot precursor spheroid. Key genes associated with ectoderm formation, such as FOXP2

[0023] , were not yet upregulated. Furthermore, while BMP7

[0022] , which is also a marker of dorsal-ventral (DV) patterning, as well as the HESX

[0024] , driver of anterior-posterior (AP) patterning, were seen to be upregulated in this stage, SHH

[0025] , driver of left-right (LR) patterning, remained silenced. These findings indicate that Anthrobots, although not initiated from embryonic cells, demonstrate some molecular hallmarks of embryonic development as they display transcriptomic signatures that align with endoderm and mesoderm induction, as well as DV and AP patterning during this initial developmental stage, while the ectoderm genes, along with a LR patterning marker continuing to stay downregulated.

[0073] Next, we considered whether the global transcriptomic changes associated with Anthrobot formation were associated with any phylogenetic shifts. To understand the age distribution of differentially expressed genes (DEGs) in Anthrobots under various conditions, we conducted a phylostratigraphic analysis

[0026] , which revealed important differences in the expression of ancient genes across conditions. We found that progenitor cells express 3812 and 1360 genes, respectively in the evolutionary ages ‘All living organisms’ and Eukaryota. For Day 0 and Day 10 bots, it is respectively 3987 and 1375, and 4004 and 1398 genes that are expressed in these two evolutionary ages. Thus, we observed an increase in the genetic expression for unicellular genes ranging from 175 genes to 209 genes in the category ‘all living organisms’ age and from 15 to 38 in Eukaryota genes (see FIG. 2E). In addition, we observed that this effect is even more pronounced when we analyzed the DEGs between the Anthrobots and their progenitor cells. Indeed, in the ‘DayO Bots vs Progenitor Cells’ and ‘Day 10 Bots vs Progenitor Cells’ conditions, where we observed respectively 429 and 602 overexpressed genes (logFC>2) for ‘all living organisms’ evolutionary age and 93 and 107 over-expressed genes (logFC>2) in the Eukaryota evolutionary age (see FIG. 2F).Atty. Dkt. No. 166118.01552

[0074] Thus, phylostratigraphic analysis reveals that in addition to their transcriptome becoming more similar to that of embryos (moving backwards in an ontogenetic sense), they also shift towards more ancient gene expression (moving backwards in a phylogenetic sense).

[0075] Anthrobots display different behaviors during the eversion process.

[0076] Anthrobots undergo eversion as they mature, which brings the cilia into an outwardfacing orientation [1], To better characterize this process (FIG. 1 A), we next investigated whether this process unfolds through a single behavioral pattern or whether there are different paths for Anthrobots to undergo eversion upon their dissolution from Matrigel®. We collected timelapse microscopy images from 30 Anthrobots during the first 48 hours upon their dissolution from Matrigel® with the goal of ascertaining its behavior over its entire trajectory. We analyzed these time course data (FIG. 3A) through a PCA analysis composed of 9 major metrics: the mean, median, and standard deviation of the angular speed, linear distance, and linear speed. When this three-dimensional data cloud was clustered using the WardD2 unsupervised clustering algorithm, we observed two statistically orthogonal (FIG. 3B) groups, each representing a distinct Anthrobot behavior during the eversion process, which we called displacers and statics (FIG. 3C). The main distinguishing factor between these two different modes of eversion is the fact that while one group (group#l : displacers) consists of bots that show some degree of motility during the eversion process (FIG. 3D-1), the other group (group #2: static bots) show no motility during this process (FIG. 3D-21). Displacement during eversion is a surprising behavior given Anthrobot motility is generated by ciliary activity, which only surfaces at the end of the eversion process. This unexpected behavior, which may be due to some bots’ ability to displace mid-eversion thanks to partial localization of ciliated cells in the bot surface could grant Anthrobots with new capabilities, such as efficient engulfment of agents of interest in the environment, (e.g., particles, filament, or debris) as shown on FIG. 3E.

[0077] Late-stage Anthrobots display markers of maturing embryonic development.

[0078] Having characterized morphogenetic rearrangements in the maturing Anthrobots, we next sought to better understand the transcriptomic landscape of this synthetic self-assembly process, as day 0 Anthrobots progress toward day 10 Anthrobots. RNA-seq analysis (FIG. 4A) revealed significant differences (FIG. 4B) that show significant changes (FIG. 4C) in the expression of FOXA1, FOXA2, FGF8, FOXP2, HESX1, SHH genes - a further remodeling ofAtty. Dkt. No. 166118.01552 gene expression past the initial changes compared to their tissue of origin (FIGs. 2A-2F). We conclude that Anthrobots undergo a second massive transcriptomic rearrangement, during their maturation.

[0079] Although the Anthrobots are made of adult human donor cells, they undergo significant morphogenetic processes, which raised the question of whether they expressed any genes associated with embryonic development despite their advanced age. The genes we were especially interested in were those involved in germ layer specification and establishment of primary body axes (FIG. 4D). In this middle stage of Anthrobot development from day 0 to day 10, we observed no considerable change in the expression of endoderm-associated genes such as FOXA1 and FOXA2, as well as a downregulation of mesoderm-driver genes such as FOXCI

[0021] , BMP7

[0022] , We did, however, find upregulation of the key genes associated with ectoderm such as FOXP2

[0023] in day 10 bots. Furthermore, HESX11

[0024] , a gene involved in anterior-posterior differentiation, becomes further upregulated in day 10 Anthrobots as well. Likewise, a key regulator of left-right patterning and neurogenesis - SSH

[0025] - which had been silent throughout earlier stages of Anthrobot formation, also gets upregulated at this later stage. Taken together, these findings indicate that Anthrobots, although not initiated from embryonic cells, demonstrate molecular hallmarks of embryonic development as they mature, including transcriptomic signatures associated with the mesoderm to ectoderm transition, as well as anterior-posterior and left-right patterning.

[0080] Anthrobots display robust behavioral longevity and reversal of epigenetic age

[0081] Having characterized the morphological and transcriptomic changes in developing and maturing Anthrobot populations, we next sought to characterize their aging profile. We collected weekly motility data from an arbitrarily chosen subset of Anthrobots to better understand behavioral profiles over time. The weekly counts of baseline behavioral activity, defined as any cilia-induced movement, exhibited consistent levels (FIG. 5A) despite the declining population. Anthrobots approaching weeks 4-8 in their life cycle experience visible structural degradation (FIG. 5B; refer to FIGs. 6A-6C for a more comprehensive analysis). However, the frequency of motile behavior among intact bots at any given time remains steady, indicating bots do not cease their movement as they age, but only stop when they die - we found no evidence of an increasing elderly, fully immobile phase of life.Atty. Dkt. No. 166118.01552

[0082] These living constructs underwent spontaneous morphogenesis and massive transcriptional remodeling with some embryonic signatures but were made of adult patients’ cells. We sought to determine the age of the Anthrobots. We decided to use human methylation aging clocks

[0016] to better understand the impact of the Anthrobot life cycle on the age of the cells comprising them (FIG. 5C). The experiment involved the use of cells from a 21 -year-old donor to create two sets of Anthrobots, aged 10 days and 25 days, respectively. Methylation clock analysis was conducted to determine the tissue age of passage 1 primary cells, yielding a calculated tissue age of 25 years based on mean values. Remarkably, comparative analysis against the donor's recorded age and that of the cells revealed a substantial reversal in the methylation clock upon differentiation into Anthrobots. Specifically, the 10-day-old Anthrobots displayed a mean age of 18.7 years, while the 25-day-old Anthrobots exhibited a mean age of 20 years. These results suggest that the Anthrobots underwent a reversal in biological age - the process of development into bots reduced their epigenetic age by 25%.

[0083] Anthrobots self-repair after mechanical damage

[0084] Finally, we asked whether Anthrobots had the capacity to repair after damage, and if so, given that this was not a normal human target morphology for any endogenous tissue or organ, what shape would they repair to. In order to test morphogenetic robustness, we damaged Anthrobots with a hypodermic needle and collected timelapse videos to characterize the response (FIG. 5D). We observed that within the first 10 to 20 minutes of damage, the Anthrobots flex, and slowly return to the original shape they were in prior to the damage. The majority of structural recovery is owed to this immediate attempt to return to the original shape. Following this immediate damage recovery, we continued imaging the damaged bots through long-term timelapse over the course of 48-72 hours and observed no further “healing” or return to original shape beyond what they had recovered in the first 20 minutes. Furthermore, we observed that Anthrobots separated into two halves by the needle don’t return to a single shape by the end of the 72 hours; they remain distinct bodies. However, the initial flexing is present in the first 20 minutes postdamage.

[0085] Out of the 23 Anthrobots that were time-lapsed immediately after being damaged, 16 showed recovery to their original spheroid shape, and 6 remained at an intermediate state (not fully indistinguishable from their original shape). One Anthrobot clearly did not show anyAtty. Dkt. No. 166118.01552 recovery. Anthrobots showed no further recovery during a longer 72-hour timelapse, and if they suffered a separation or a split during this period, they did not reform into a single whole but stayed in distinct pieces.

[0086] Regardless of initial size, Anthrobots degrade at a steady rate, resulting in large bots living longer

[0087] Anthrobots begin to degrade as they age, shrinking in size and losing displacement ability. [1], To better characterize Anthrobot aging and degradation, we next investigated whether this process unfolds through a single behavioral pattern or whether there are different paths for Anthrobots to degrade. We collected timelapse microscopy images from 36 Anthrobots during the first 35 days upon their dissolution from Matrigel® with the goal of characterizing their degradation rate. We analyzed these time course data (FIG. 6A) through a PCA analysis composed of 8 major metrics: Initial Area (Area of bot in 1st Frame), Fin Area (Area of Bot in final trackable frame), Change in Area (difference between Fin Area and Initial Area), Percent Change (Change in Area / Initial Area * 100), Standard Deviation of Difference in Areas (SD of Change in Area between successive frames), Mean of Difference in Areas (Mean of Change in Area between successive frames), Time to disintegrate (Frames until the bot becomes untrackable), and ChangeRate (the maximum difference in the difference of areas, akin to the second derivative for the area). When this three-dimensional data cloud was clustered using the WardD2 unsupervised clustering algorithm, we observed two statistically orthogonal (FIG. 6B) groups, revealing a positive correlation between bots’ initial morphology and their life span. Regardless of initial morphology, all Anthrobots are subject to a similar rate of degradation, resulting in large bots living longer than smaller Anthrobots. (FIG. 6C) From this analysis we learn that Anthrobot initial morphology has a significant impact on Anthrobot longevity, as opposed to there being a definite life span across all population.

[0088] Discussion

[0089] Here, we focused on investigating the morphological, behavioral, and transcriptomic life stages of Anthrobots. We started by examining the self-construction process of Anthrobots: as they mature, Anthrobots undergo eversion, bringing the cilia into an outward-facing orientation. We found three distinct paths by which Anthrobots can self-construct when embedded in Matrigel, illustrating a degree of variability / individuality in their morphogenesis. We also foundAtty. Dkt. No. 166118.01552 that Anthrobots can recover their functional form after drastic mechanical injury, which could make them a useful model for screening healing interventions or investigating the differences in self-repair from cells of donors of different age. The fact that they self-repair is likely an emergent feature of generic epithelial dynamics

[0027] , because Anthrobots were never part of an evolutionary process in which ability to repair themselves was selected for.

[0090] A key question in evolutionary developmental biology concerns the relationship between the molecular-genetic and morpho-behavioral information, and possible bi-directional control loops between them. Given that gene expression is under strong selection forces by a history in a specific environment, we investigated the transcriptome of a motile, self-assembling novel living construct. We explored the genes expressed by Anthrobots and compare them to those of their parent tissue (airway epithelium) or human embryos to understand the plasticity of cell groups in the absence of genomic editing. We found that Anthrobots exhibit a much-altered transcriptome and display embryonic markers, especially genes involved in axial patterning. Future studies using spatial transcriptomics will be needed to determine whether cryptic tissuelevel polarity or other kinds of biochemical patterning exists in the Anthrobots. More broadly, numerous kinds of synthetic constructs, such as more conventional scaffold-based biobots [10g, 28], should be examined via mRNA- and protein-level omics approaches to look for novel gene expression profiles induced by morphological configuration and behavior alone.

[0091] To understand the life cycle of this novel synthetic construct, we collected weekly motility data and behavioral profiles of aging Anthrobots; they displayed robust behavioral longevity, remaining motile until the very end of their lifespan, at which point they degrade by spontaneously dissociating. We investigated the aging and degradation of Anthrobots, finding that regardless of their initial size, Anthrobots degrade at a steady rate, resulting in larger bots living longer. Information on their end of life is important for potential in vivo uses of Anthrobots in patients for pro-regenerative applications

[0029] , We also believe in vitro Anthrobots provide an attractive platform for screening anti-aging candidate interventions, because they can be made patient-specific, and because unlike traditional cell culture and organoid models, they offer motility, which can be used to quantify effects of putative pro-longevity reagents not only on anatomical and molecular markers, but on active behavior. On-going studies will characterize that behavior with respect to ability to follow gradients (preferences) and ability to learn in simple assays

[0030] ,Atty. Dkt. No. 166118.01552

[0092] The Anthrobots have a somewhat unique life history because the age of their cells does not match the age of their anatomical structure: they are made of adult (even elderly) cells, but their life as a multicellular creature begins anew. Thus, we sought to understand what impact their new morphogenetic configuration might have on cell-level markers of aging, via an epigenetic clock approach

[0031] , This revealed that fully-assembled and motile multicellular Anthrobots are younger than their cells of origin. This is notable because it did not require any of the expected rejuvenation treatment and occurred in the absence of up-regulation (forced or natural) of the Yamanaka reprogramming factors. A similar event has been found in embryos

[0032] , and it has been seen that senescence can be reduced by 3D culture

[0033] , Our data with adult-derived cells reveal that reduction of epigenetic age by morphogenetic processes may not be an exclusive, programmed feature of embryonic development but could be generically induced by morphogenetic processes. One way to think about this is from the perspective of the cells as information-processing agents which integrate signals from their internal and external environment. On the one hand, these cells had molecular components consistent with decades of adult life. On the other hand, all their current evidence - transcription of embryonic axial patterning genes, mechanical stresses of morphogenesis, etc. point to being part of an embryo. In the current protocol, this conflicting information induces a moderate roll-back of the epigenetic clock. To improve this effect, with possible implications for the field of longevity, we suggest a research roadmap that exploits recent advances in extending neural decision-making dynamics to understand cell responses

[0034] , Frameworks such as active inference could be used to guide interventions to cell signaling

[0035] to modify cells’ internal model of their own age.

[0093] There were a number of limitations in this study. Metabolic profiles and consequences of this self-assembly process are still unknown. Likewise, the factors responsible for the individuality of the bots (the variety of their behaviors, life span, etc.) are unknown. With respect to behavior, at this point we do not know the rules governing their motion, but experiments in enriched environments with living and non-living features will help to probe their behavioral repertoire and thus expand our understanding of basal cognition (especially in evolutionarily-novel forms), and provide additional endpoints for screening of anti-aging and nootropic compounds. Future work will also characterize Anthrobots’ biomechanical and bioelectrical properties, and the use of stimuli during their self-construction process to guide form and function.Atty. Dkt. No. 166118.01552

[0094] Synthetic living constructs made of the patient’s own cells have the potential to impact biomedicine and in vitro organ bioengineering. Especially important is to augment studies of native biology in organoid models with ones that examine the emergent motile behaviors and nascent life histories, so that their properties and competencies can be predicted, exploited, and shaped toward useful applications. By characterizing new ways in which adult somatic cells can reboot their multicellularity, and understanding the way these wild-type cells navigate transcriptional, morphological, and behavioral spaces, novel aspects of the cell-body relationship are revealed. Novel transcriptomic profiles, driven by configurations and lifestyle, as well as behaviors not exhibited in the standard species-specific morphological trajectory, have implications for basic evolutionary, cell, and developmental biology and the appearance of novelty and evolvability

[0036] ,

[0095] Methods

[0096] Imaging for Material Growth (Growth of Bots in Matrigel® with NLS)

[0097] The Normal Human Bronchial Epithelial (NHBE) cells were seeded into a Matrigel® suspension according to the previously established protocol [1], Once in the Matrigel® suspension, select NHBEs (15-20 beacons / Points of Interest) were imaged using an EVOS M7000 (AMF7000) once every 3 to 4 days using a 20x objective in both Brightfield and RFP until they had been 14 days in the Matrigel®, at which point they were removed from the Matrigel® suspension and used in downstream applications.

[0098] Statistical analysis of Anthrobot growth modalities

[0099] To find if we could computationally identify any patterns in the assembling of bots, we used Fiji to track the change in the size of the "bot area" for each frame using the AnalyzeParticles tool. After getting a CSV of each of the 30 bots, we decided that experimental observations had three general categories we needed to distinguish between (1) bots that senesced i.e died and stopped growing in size (2) grew completely from one cell to a bot and thus had no merges (3) ones that merged multiple cellular entities to form the final bot that grew to the final size. To distinguish these relationships, we generated three variables from the data, namely: (i) merge events: the number of times distinct spheroids merged to distinguish the bots that formed via mechanism (3) from those that formed via mechanism (1) and (2). (ii) fin area: the final area of the bot at the end of the growing process which helps distinguish bots formed via mechanismAtty. Dkt. No. 166118.01552(1) from those formed via mechanism (2) and (3) since senesced hots ought to have significantly smaller area than other categories which grew to completion. (Hi) sdarea: the standard deviation of consecutive differences in area, i.e the variation in the rate of change of area over time, which helped separate mechanism (1) from those formed via mechanism (2) and (3) since senesced bots ought to have smaller rate of change after it senesces (close to zero). This redundancy reinforces the results of the fin area variable. These three variables were then used in a Principal Components Analysis (PCA), which had -90% variation in the first two components. We then used hierarchical clustering using the Ward.D2 method to derive three clusters, which seemed to correlate in identity to the three types of bots we wanted to distinguish.

[0100] RN A Extraction

[0101] RNA was extracted from Anthrobots and Normal Human Bronchial Epithelial (NHBEs) cells under different time points and experimental conditions. The extractions were performed using Invitrogen’s TRIzol Reagent user guide as a reference. For the NHBEs, the only additional step was to trypsinize the cell layer with .05% Trypsin for 3-4 minutes until the cells had been suspended in their media. Once the material (Anthrobots and NHBEs) was in suspension, it was collected into 15mL conical tubes and centrifuged at 300g for 3 minutes. The supernatant was then removed, and the material was resuspended in ImL of TRIzol and pipetted up and down to homogenize. Next, 0.2mL of Chloroform was added and then the sample was centrifuged for 15 minutes at 12,000g and 4°C. After centrifugation, the aqueous phase containing the RNA was transferred to a new tube. 0.5mL of isopropanol was added to the aqueous phase and incubated at room temperature for 10 minutes. After incubation, the sample was centrifuged for 10 minutes at 12,000g and 4°C. The RNA formed a small, white pellet on the bottom of the tube. Next, the supernatant was discarded, being careful not to disturb the RNA pellet. The pellet was then resuspended in 75% ethanol, vortexed briefly, and centrifuged again for 5 minutes at 7,500g and 4°C. The supernatant was then discarded, and the pellet was left to dry for between 5-10 minutes. After drying, 50pL of RNAse-free water was added to the pellet, and the sample was incubated on a heat block at 55°C for 10 minutes. Finally, the RNA concentration in the sample was measured using a NanoDrop One from Thermo Fisher Scientific.

[0102] rRNA depletion RNA-sequencingAtty. Dkt. No. 166118.01552

[0103] RNA quality was assessed via bioanalyzer at the Tufts Genomic Core, and Illumina Stranded Total RNA with Ribo-Zero Plus was used for library preparation with high-quality RNA. Upon library multiplexing, on Illumina HiSeq 2500, an rRNA depletion run using reversely- stranded, single-end sequencing was performed.

[0104] NGS and pathway analysis

[0105] Reads were trimmed for adapter“GATCGGAAGAGCACACGTCTGAACTCCAGTCAC” (SEQ ID NO: 1) and polyX tails, then filtered by sequencing Phred quality (>=Q15) using fastp

[0037] , A count table was generated by aligning reads to the human transcriptome (Ensembl version 110) using kallisto

[0038] and converting transcript counts to gene counts using tximport

[0039] , To filter out low expressing genes, we kept genes that have counts per million (CPM) more than 0.29 in at least 3 samples. We use this CPM threshold so that we keep genes that are expected to have at least 10 counts, which is a rule-of-thumb value. There were 22518 genes after filtering. We then normalized counts by weighted trimmed mean of M-values (TMM)

[0040] , If no normalization was needed, all the normalization factors would be 1. Here the normalization factors were between 0.84 and 1.26. To use linear models in the following analysis, we performed Voom transformation

[0041] to transform counts into logCPM, where logCPM=log2(106*count / (library sizexnormalization factor)). Voom transformation estimated the mean-variance relationship and used it to compute appropriate observation-level weights so that more read depth gave more weights. To get an overall view of the similarity and / or difference of the samples, we performed principal component analysis (PCA). To discover the differential genes, we used limma, an R package that powers differential expression analyses

[0042] , We performed linear modeling and moderated t-tests to detect genes that were differentially expressed between groups. The GO database has hierarchical structures so that we could group the GO terms by their main (parent) categories

[0017] , We performed GSEA in the GO database

[0043] , Enriched GO terms were identified at a significance threshold of adjusted p- value < 0.05

[0044] , We constructed a network of the significant GO terms and refine the weights of edges based on the Jaccard similarity

[0045] , We then identify pathway communities (i.e. clusters) by multi-level modularity optimization i.e. Louvain algorithm

[0046] , We selected one GO term that has the smallest p-value in GSEA for each community. We performed GSEA in the Reactome database

[0047] , Enriched pathways were identified at a significance threshold of adjusted p-value < 0.05

[0044] , We constructed a network of the significant Reactome pathways and refine the weightsAtty. Dkt. No. 166118.01552 of edges based on the Jaccard similarity

[0045] , We then identify pathway communities (i.e. clusters) by multi-level modularity optimization i.e. Louvain algorithm

[0046] , We selected one Reactome pathway that has the smallest p-value in GSEA for each community. We performed GSEA in the GTRD database

[0048] , Enriched transcription factors were identified at a significance threshold of adjusted p-value < 0.05

[0044] , We constructed a network of the significant transcription factors and refine the weights of edges based on the Jaccard similarity

[0045] , We then identify transcription factor communities (i.e. clusters) by multi-level modularity optimization i.e. Louvain algorithm

[0046] , We selected one transcription factor that has the smallest p-value in GSEA for each community.

[0106] Phylostrati graphic analysis of the differentially expressed genes (DEGs)

[0107] In order to achieve the phylostratigraphic analysis of the differentially expressed genes (DEGs) of the Anthrobots in the different conditions, we first extracted the evolutionary ages of all of of the 19,660 protein-coding human genes from Litman et al.

[0049] Litman et al. assigned these ages to 1 of the 19 major phylostrata defined by Domazet-Loso and Tautz

[0050] , These phylostratas are the following: All living organisms, (Eubacteria, bacteria and their descendants), Eukaryota, Opisthokonta, Holozoa, Metazoa, Eumetazoa, Bilateria, Deuterostomia, Chordata, Olfactores, Craniata, Euteleostomi, Tetrapoda, Amniota, Mammalia, Eutheria, Boreoeutheria, Euarchontoglires, and Primates. We selected the first two to assess how much the Anthrobots are behaving like unicellular organisms in terms of genetic expression and counted the number of genes expressed in the different conditions.

[0108] Comparison with morula genes

[0109] We extracted the list of morula genes from Yan et al.

[0051] , To do so, we averaged the genetic expression of each cell at the morula stage and kept only the genes with RPKM>2. We computed the overlap between the DEGS in the different conditions with this list of morula genes. Then, we enriched the overlapped list of genes using g:Profiler

[0052] ,

[0110] Imaging for Polarity Reversal Experiment[0U1] The imaging for the polarity reversal was done using Anthrobots recently dissolved from their Matrigel® suspension in order to capture their inversion from Apical-in to Apical-out. The setup we used involved a Bioscience Tools Stage Incubation system consisting of a CO2Atty. Dkt. No. 166118.01552Mixer (C02-MI), a top and bottom stage incubator (TC-MWPHB), a temperature controller (TC- l-100i), and a humidifier (C02-500ml). This was set up on an EVOS FL Auto microscope (AMAFD1000) used for all the imaging. After the imaging setup, the Anthrobots were removed from their Matrigel® suspension following the protocol in the original Anthrobots paper (Motile Living Biobots Self-Construct from Adult Human Somatic Progenitor Seed Cells). 200pL of BEDM (Bronchial Epithelial Differentiation Media) was added to the inner wells of a 96-well, ultra-low attachment, round-bottom plate from Corning (Cat no. 7007). The outer wells were filled with sterile DiH20, which we found critical to prevent evaporation in the stage incubation set-up. Once the plate was filled with media, the bots were moved to individual wells in a 96-well ultralow attachment, a round-bottom plate from Corning (Cat no. 7007). With one bot per well, the plate was placed into the stage incubation setup. The wells were imaged at lOx every hour for 72 hours.

[0112] PCA clustering of polarity reversal time course

[0113] To find if we could computationally identify any patterns in the behavior of bots during the polarity reversal stage, we used Fiji to track the change in position of the bot for each frame using the AnalyzeParticles tool. After getting a coordinate analysis of each of the bots, we generated a few variables from the data. Initially, to determine the trajectory of the bot, the linear distance, speed, heading, and angular speed were estimated at each position to predict the coordinates of the following position. Of these variables, we took the mean, median and standard deviation of the angular speed, linear distance, and linear speed over the entire trajectory of the bot to try to ascertain its behavior over its entire trajectory. These 9 variables were part of the variables used to make the PCA. We then derived two metrics to describe each trajectory: i) a “straightness” index computed as 1 minus the circular variance of the headings during the block (a value of 1 indicates a perfectly straight line) and ii) a “gyration” index computed as 1 minus the circular variance of the angular speed during the block divided by the circular variance of the same angular speeds and their additive inverse, which helps in taking into account the magnitude of the angular speeds themselves (a value of 1 indicates a trajectory following a perfect circle). These eleven variables were then used in a Principal Components Analysis (PCA), which had -57% variation in the first two components. We then used hierarchical clustering using the Ward.D2 method to derive two clusters.Atty. Dkt. No. 166118.01552

[0114] DN A Extracti on

[0115] DNA was extracted from Anthrobots and Normal Human Bronchial Epithelial (NHBEs) cells using a QIAGEN DNeasy Blood and Tissue Kit with its protocol as a reference. All buffers and mixtures were provided by the kit, save for PBS. For the NHBEs, the only additional step was to trypsinize the cell layer with 0.05% Trypsin for 3-4 minutes until the cells had been suspended in their media. Once the material (Anthrobots and NHBEs) was in suspension, it was collected into 15mL conical tubes and centrifuged at 300g for 3 minutes. The supernatant was then removed, and the material was resuspended in 200pL of PBS. Then 20pL of Proteinase K was added to the material in suspension. Next, 200pL of AL Buffer was added to the suspension and was mixed by vortexing. Next, 200pL of 95%-l 00% ethanol was added to the suspension, and the solution was again mixed by vortexing. The mixture was then pipetted into a spin column placed inside a 2mL collection tube and centrifuged at 6,000g for 1 minute. Then, after discarding the flow-through, 500pL of Buffer AW1 was added, and the column was centrifuged again at 6,000g for 1 minute. Then, the flow-through was discarded, and 500pL of Buffer AW2 was added to the column. The column was then centrifuged for 3 minutes at 20,000g; this centrifugation was repeated to dry the column. After the dry spin, the column was placed in a fresh 1.7mL tube, and 200pL of AE Buffer was added. It was let to sit for 1 minute before centrifuging for a final time at 6,000g for 1 minute. Afterward, the yield of the DNA collected in the 1 ,7mL tube was measured using a NanoDrop One from Thermo Fisher and stored at 4°C until needed for downstream applications.

[0116] DNA methylation age analysis

[0117] The DNA methylation array assays were performed using the following individual components. First bisulfite conversion of extracted DNA using a Zymo EZ DNA methylation kit for downstream processing by either DNA methylation array technology. Followed by Methyl ationEPIC BeadChip array kits, which include all the reagents required for DNA amplification, labeling, and array. The third step was to scan completed Beadchip arrays for the final DNA methylation readout. Finally statistical analysis of measures of epigenetic aging for the respective species tested, an analysis of treatment vs. control conditions, and an analysis of any available meta-data for associations with changes to epigenetic aging.

[0118] Damage ExperimentAtty. Dkt. No. 166118.01552

[0119] Anthrobots were grown according to the previously established protocol [1] and were transferred between days 2 and 11 into BEDM containing wells of a 96-well ultra-low attachment, a round-bottom plate from Corning (Cat no 7007). The outer wells were fdled with sterile DiH20, which we found critical to prevent evaporation in the stage incubation set-up. Next, bots were damaged with a hypodermic needle and upon withdrawing the needle, they were imaged immediately every 2 seconds for 10 to 20 minutes at lOx magnification using a Zeiss Axio Observer, equipped with an Axiocam 506 mono camera. The following longer term timelapses were acquired within a Bioscience Tools Stage Incubation system, consisting of a CO2 Mixer (C02-MI), a top and bottom stage incubator (TC-MWPHB), a temperature controller (TC-l-100i), and a humidifier (C02-500ml), using an EVOS FL Auto microscope (AMAFD1000). The wells were imaged at lOx every 30 minutes for between 48 and 72 hours.

[0120] Imaging for Degradation

[0121] The Anthrobots were imaged for their degradation in the following manner. First, the Anthrobots were dissolved from their Matrigel® suspension according to the previously established protocol[l], then each well of a 96-well ultra-low attachment round-bottom plate from Corning (Cat no. 7007) was filled with 200uL of Bronchial Epithelial Differentiation Media (BEDM) with Retinoic acid. After adding the media, Anthrobots were added individually to each well, resulting in 1 bot per well. The bots were imaged every 3-4 days at 20x in Brightfield using a Zeiss Axio Observer.Zl, equipped with an Axiocam 506 mono camera until they disintegrated to the point where no spheroid structure was visible. Media changes were performed every 4 days by aspirating lOOpL of media from each well and then adding lOOpL of fresh BEDM with Retinoic acid.

[0122] Statistical identification of degradation clusters

[0123] In order to computationally identify any patterns in the bot degradation stage, we used Fiji to track the change in position, area, perimeter and other physical parameters of the bot for each frame using the AnalyzeParticles tool. After getting a coordinate index of 36 bots, we generated a few variables from the data, focused mainly on deriving insights from the area of the main bot, so that change of area could tell us about rates of degradation. These include Initial Area (Area of bot in 1st Frame), Fin Area (Area of Bot in final trackable frame), Change in Area (Fin Area - Initial Area), Percent Change (Change in Area / Initial Area * 100), SI) of Difference inAtty. Dkt. No. 166118.01552Areas (SD of Change in Area between successive frames), Mean of Difference in Areas (Mean of Change in Area between successive frames), Time to disintegrate (Frames till the bot is untrackable), ChangeRate (The maximum difference in difference of areas, akin to second derivative for area). These eight variables were then used in a Principal Components Analysis (PCA) which had -72% of variation in the first two components. We then used hierarchical clustering using the Ward.D2 method to derive three clusters. The goal of these eight variables was to segregate disintegrating bots based on speed and raw changes in size. The number of clusters, three, were chosen to try to highlight modes of degradation that had (1) smaller bots rapidly disintegrating to nothing (2) larger bots slowly disintegrating and (3) had a combination of the two rates of degradation.

[0124] References[1] G. Gumuskaya, P. Srivastava, B. G. Cooper, H. Lesser, B. Semegran, S. Garnier, M. Levin, Adv Sci (Weinh) 2023, e2303575, doi.org / 10.1002 / advs.202303575.[2] M. Wehner, R. L. Truby, D. J. Fitzgerald, B. Mosadegh, G. M. Whitesides, J. A. Lewis, R. J. Wood, Nature 2016, 536 (7617), 451, doi.org / 10.1038 / naturel 9100.[3] Q. Ze, S. Wu, J. Dai, S. Leanza, G. Ikeda, P. C. Yang, G. laccarino, R. R. Zhao, Nature Communications 2022, 13 (1), 3118, doi.org / 10.1038 / s41467-022-30802-w.[4] C. Dillinger, N. Nama, D. Ahmed, Nature Communications 2021, 12 (1), 6455, doi.org / 10.1038 / s41467-021 -26607-y .[5] M. Z. Miskin, A. J. Cortese, K. Dorsey, E. P. Esposito, M. F. Reynolds, Q. Liu, M. Cao, D. A. Muller, P. L. McEuen, I. Cohen, Nature 2020, 584 (7822), 557, doi.org / 10.1038 / s41586- 020-2626-9.[6] a) W. Hu, G. Z. Lum, M. Mastrangeli, M. Sitti, Nature 2018, 554 (7690), 81, doi.org / 10.1038 / nature25443; b) A. Li, H. Li, Z. Li, Z. Zhao, K. Li, M. Li, Y. Song, Science Advances 2020, 6 (7), eaay5808, doi.org / 10.1126 / sciadv.aay5808; c) R. H. Soon, Z. Yin, M. A. Dogan, N. O. Dogan, M. E. Tiryaki, A. C. Karacakol, A. Aydin, P. Esmaeili-Dokht, M. Sitti, Nat Commun 2023, 14 (1), 3320, doi.org / 10.1038 / s41467-023-38689-x; d) H. Lu, H. Cui, G. Lu, L. Jiang, R. Hensleigh, Y. Zeng, A. Rayes, M. K. Panduranga, M. Acharya, Z. Wang, A. Irimia, F. Wu, G. P. Carman, J. M. Morales, S. Putterman, L. W. Martin, Q. Zhou, X. R. Zheng, Nat Commun 2023, 14 (1), 2418, doi.org / 10.1038 / s41467-023-37335-w; e) A. Kotikian, C. McMahan, E. C. Davidson, J. M. Muhammad, R. D. Weeks, C. Daraio, J. A. Lewis, Sci Robot 2019, 4 (33), doi.org / 10.1126 / scirobotics.aax7044; f) X. Yang, R. Tan, H. Lu, T. Fukuda, Y. Shen, Nat Commun 2022, 13 (1), 4156, doi.org / 10.1038 / s41467-022-31913-0.[7] a) A. Menciassi, S. Takeuchi, R. D. Kamm, APT Bioengineering 2020, 4 (2), 020401, doi.org / 10.1063 / 5.0014918; b) L. Ricotti, B. Trimmer, A. W. Feinberg, R. Raman, K. K. Parker, R. Bashir, M. Sitti, S. Martel, P. Dario, A. Menciassi, Science Robotics 2017, 2 (12), eaaq0495, doi.org / 10.1126 / scirobotics.aaq0495.Atty. Dkt. No. 166118.01552[8] J. C. Nawroth, H. Lee, A. W. Feinberg, C. M. Ripplinger, M. L. McCain, A. Grosberg, J.O. Dabiri, K. K. Parker, Nature Biotechnology 2012, 30 (8), 792, doi.org / 10.1038 / nbt.2269.[9] S.-J. Park, M. Gazzola, K. S. Park, S. Park, V. Di Santo, E. L. Blevins, J. U. Lind, P. H. Campbell, S. Dauth, A. K. Capulli, F. S. Pasqualini, S. Ahn, A. Cho, H. Yuan, B. M. Maoz, R. Vijaykumar, J.-W. Choi, K. Deisseroth, G. V. Lauder, L. Mahadevan, K. K. Parker, Science 2016, 353 (6295), 158, doi.org / 10.1126 / science.aaf4292.

[0010] a) R. Raman, C. Cvetkovic, S. G. Uzel, R. J. Platt, P. Sengupta, R. D. Kamm, R. Bashir, Proc Natl Acad Sci USA 2016, 113 (13), 3497, doi.org / 10.1073 / pnas.1516139113; b) J. Xi, J. J. Schmidt, C. D. Montemagno, Nat Mater 2005, 4 (2), 180, doi.org / 10.1038 / nmatl308; c) A. W. Feinberg, A. Feigel, S. S. Shevkoplyas, S. Sheehy, G. M. Whitesides, K. K. Parker, Science 2007, 317 (5843), 1366, doi.org / 10.1126 / science.1146885; d) S. Das, E. E. Hunter, N. A. DeLateur, E. B. Steager, R. Weiss, V. Kumar, Journal of Micro-Bio Robotics 2019, 15 (2), 79, doi.org / 10.1007 / sl2213-019-00119-x; e) M. S. Sakar, D. Neal, T. Boudou, M. A. Borochin, Y. Li, R. Weiss, R. D. Kamm, C. S. Chen, H. H. Asada, Lab on a Chip 2012, 12 (23), 4976, doi.org / 10.1039 / c21c40338b; f) B. J. Williams, S. V. Anand, J. Rajagopalan, M. T. Saif, Nat Commun 2014, 5, 3081, doi.org / 10.1038 / ncomms4081; g) O. Aydin, X. Zhang, S. Nuethong, G. J. Pagan-Diaz, R. Bashir, M. Gazzola, M. T. A. Saif, Proc Natl Acad Sci USA 2019, 116 (40), 19841, doi.org / 10.1073 / pnas.1907051116.

[0011] a) D. Blackiston, E. Lederer, S. Kriegman, S. Garnier, J. Bongard, M. Levin, Science Robotics 2021, 6 (52), eabfl571, doi.org / 10.1126 / scirobotics.abfl571; b) S. Kriegman, D. Blackiston, M. Levin, J. Bongard, Proceedings of the National Academy of Sciences 2020, 117 (4), 1853, doi.org / 10.1073 / pnas.1910837117; c) Proceedings of the National Academy of Sciences 2021, 118 (49), e2112672118, doi.org / 10.1073 / pnas.2112672118.

[0012] a) C. E. Barkauskas, M.-I. Chung, B. Fioret, X. Gao, H. Katsura, B. L. M. Hogan, Development 2017 144 (6), 986, doi.org / 10.1242 / dev.140103; b) M. Hild, A. B. Jaffe, Current Protocols in Stem Cell Biology 2016, 37 (1), doi.org / 10.1002 / cpsc.l; c) S. Hasan, P. Sebo, R. Osicka, The FEBS Journal 2018, 285 (23), 4343, doi.org / 10. l l l l / febs.14582; d) J. Zhou, C. Li, N. Sachs, M. C. Chiu, B. H. Wong, H. Chu, V. K. Poon, D. Wang, X. Zhao, L. Wen, W. Song, S. Yuan, K. K. Wong, J. F. Chan, K. K. To, H. Chen, H. Clevers, K. Y. Yuen, Proc Natl Acad Sci USA 2018, 115 (26), 6822, doi.org / 10.1073 / pnas.1806308115; e) N. Sachs, A. Papaspyropoulos, D. D. Zomer-van Ommen, I. Heo, L. Bottinger, D. Klay, F. Weeber, G. Huelsz-Prince, N. lakobachvili, G. D. Amatngalim, J. Ligt, A. Hoeck, N. Proost, M. C. Viveen, A. Lyubimova, L. Teeven, S. Derakhshan, J. Korving, H. Begthel, J. F. Dekkers, K. Kumawat, E. Ramos, M. F. Oosterhout, G. J. Offerhaus, D. J. Wiener, E. P. Olimpio, K. K. Dijkstra, E. F. Smit, M. Linden, S. Jaksani, M. Ven, J. Jonkers, A. C. Rios, E. E. Voest, C. H. Moorsei, C. K. Ent, E. Cuppen, A. Oudenaarden, F. E. Coenjaerts, L. Meyaard, L. J. Bont, P. J. Peters, S. J.Tans, J. S. Zon, S. F. Boj, R. G. Vries, J. M. Beekman, H. Clevers, The EMBO Journal 2019, 38 (4), doi.org / 10.15252 / embj .2018100300; f) G. R. Ramirez-San Juan, A. J. T. M. Mathijssen, M. He, L. Jan, W. Marshall, M. Prakash, Nature Physics 2020, 16 (9), 958, doi.org / 10.1038 / s41567- 020-0923-8.

[0013] C. A. Boecking, P. Walentek, L. T. Zlock, D. I. Sun, P. J. Wolters, H. Ishikawa, B.-J. Jin,P. M. Haggie, W. F. Marshall, A. S. Verkman, W. E. Finkbeiner, American Journal of Physiology-Lung Cellular and Molecular Physiology 2022, 322 (3), L420, doi . org / 10.1152 / aj plung.00536.2020.Atty. Dkt. No. 166118.01552

[0014] G. Stroulios, T. Brown, G. Moreni, D. Kondro, A. Dei, A. Eaves, S. Louis, J. Hou, W. Chang, D. Pajkrt, K. C. Wolthers, A. Sridhar, S. Simmini, Sc i Rep 2022, 12 (1), 7673, doi.org / 10.1038 / s41598-022- 11700-z.

[0015] P. Wijesekara, P. Yadav, L. A. Perkins, D. B. Stolz, J. M. Franks, S. C. Watkins, E. R. Jacome, S. L. Brody, A. Horani, J. Xu, A. B. Farimani, X. Ren, 2022, doi . org / 10.1101 / 2022.01.15.476455.

[0016] a) S. Horvath, Genome Biol2013, 74 (10), R115, doi.org / 10.1186 / gb-2013-14-10-rl l5; b) S. Horvath, J. Oshima, G. M. Martin, A. T. Lu, A. Quach, H. Cohen, S. Felton, M. Matsuyama, D. Lowe, S. Kabacik, J. G. Wilson, A. P. Reiner, A. Maierhofer, J. Flunkert, A. Aviv, L. Hou, A. A. Baccarelli, Y. Li, J. D. Stewart, E. A. Whitsei, L. Ferrucci, S. Matsuyama,K. Raj, Aging (Albany NY) 2018, 10 (7), 1758, doi.org / 10.18632 / aging.101508; c) S. Horvath, A. T. Lu, H. Cohen, K. Raj, Aging (Albany NY) 2019, 11 (10), 3238, doi.org / 10.18632 / aging.101976.

[0017] M. A. Harris, J. Clark, A. Ireland, J. Lomax, M. Ashburner, R. Foulger, K. Eilbeck, S. Lewis, B. Marshall, C. Mungall, J. Richter, G. M. Rubin, J. A. Blake, C. Bult, M. Dolan, H. Drabkin, J. T. Eppig, D. P. Hill, L. Ni, M. Ringwald, R. Balakrishnan, J. M. Cherry, K. R. Christie, M. C. Costanzo, S. S. Dwight, S. Engel, D. G. Fisk, J. E. Hirschman, E. L. Hong, R. S. Nash, A. Sethuraman, C. L. Theesfeld, D. Botstein, K. Dolinski, B. Feierbach, T. Berardini, S. Mundodi, S. Y. Rhee, R. Apweiler, D. Barrell, E. Camon, E. Dimmer, V. Lee, R. Chisholm, P. Gaudet, W. Kibbe, R. Kishore, E. M. Schwarz, P. Sternberg, M. Gwinn, L. Hannick, J.Wortman, M. Berriman, V. Wood, N. de la Cruz, P. Tonellato, P. Jaiswal, T. Seigfried, R. White, C. Gene Ontology, Nucleic Acids Res 2004, 32 (Database issue), D258, doi.org / 10.1093 / nar / gkh036.

[0018] R. S. Beddington, E. J. Robertson, Cell 1999, 96 (2), 195, doi.org / 10.1016 / s0092- 8674(00)80560-7.

[0019] A. T. Muhr J, Ackerman KM. , Embryology, Gastrulation, StatPearls Publishing, www.ncbi.nlm.nih.gov / books / NBK554394 / 2023.

[0020] a) H. Wan, S. Dingle, Y. Xu, V. Besnard, K. H. Kaestner, S. L. Ang, S. Wert, M. T. Stahlman, J. A. Whitsett, JBiolChem 2005, 280 (14), 13809, doi.org / 10.1074 / jbc.M414122200; b) Y. E. Mavromatakis, W. Lin, E. Metzakopian, A. L. Ferri, C. H. Yan, H. Sasaki, J. Whisett, S.L. Ang, Mech Dev 2011, 725 (1-2), 90, doi.org / 10.1016 / j.mod.2010.11.002.

[0021] B. Wilm, R. G. James, T. M. Schultheiss, B. L. Hogan, Dev Biol 2004, 271 (1), 176, doi.org / 10.1016 / j .ydbio.2004.03.034.

[0022] Y. Yan, Q. Wang, Front Cell Dev Biol 2021, 9, 799772, doi.org / 10.3389 / fcell.2021.799772.

[0023] D. Evseenko, Y. Zhu, K. Schenke-Layland, J. Kuo, B. Latour, S. Ge, J. Scholes, G. Dravid, X. Li, W. R. MacLellan, G. M. Crooks, Proc Natl Acad Sci USA 2010, 107 (31), 13742, doi.org / 10.1073 / pnas.1002077107.

[0024] C. L. Andoniadou, M. Signore, E. Sajedi, C. Gaston-Massuet, D. Kelberman, A. J. Bums, N. Itasaki, M. Dattani, J. P. Martinez-Barbera, Development 2007, 134 (8), 1499, doi . org / 10.1242 / dev .02829.Atty. Dkt. No. 166118.01552

[0025] S. M. Pagan-Westphal, C. J. Tabin, Cell 1998, 93 (1), 25.

[0026] a) T. Domazet-Loso, J. Brajkovic, D. Tautz, Trends Genet 2007, 23 (11), 533, doi.org / 10.1016 / j.tig.2007.08.014; b) Z. Arendsee, J. Li, U. Singh, A. Seetharam, K. Dorman, E. S. Wurtele, Bioinformatics 2019, 35 (19), 3617, doi.org / 10.1093 / bioinformatics / btzl71.

[0027] L. A. Davidson, Trends Cell Biol 2012, 22 (2), 82, doi.org / 10.1016 / j.tcb.2011.10.005.

[0028] a) R. Doursat, C. Sanchez, Soft Robotics 2014, 1 (2), 110; b) R. Doursat, H. Sayama, O. Michel, Nat Comput 2013, 12 (4), 517, doi.org / Doi 10.1007 / S11047-013-9398-1; c) M. Sample, M. Boulicault, C. Allen, R. Bashir, I. Hyun, M. Levis, C. Lowenthal, D. Mertz, N. Montserrat, M. J. Palmer, K. Saha, J. Zartman, Biofabrication 2019, 11 (4), 043001, doi.org / 10.1088 / 1758- 5090 / ab268c; d) G. J. Pagan-Diaz, K. P. Ramos-Cruz, R. Sam, M. E. Kandel, O. Aydin, M. T. A. Saif, G. Popescu, R. Bashir, Proc Natl Acad Sci USA 2019, 116 (51), 25932, doi.org / 10.1073 / pnas.1916138116; e) R. D. Kamm, R. Bashir, N. Arora, R. D. Dar, M. U. Gillette, L. G. Griffith, M. L. Kemp, K. Kinlaw, M. Levin, A. C. Martin, T. C. McDevitt, R. M. Nerem, M. J. Powers, T. A. Saif, J. Sharpe, S. Takayama, S. Takeuchi, R. Weiss, K. M. Ye, H. G. Yevick, M. H. Zaman, Apl Bioeng 2018, 2 (4), 040901, doi.org / 10.1063 / 1.5038337; f) R. D. Kamm, R. Bashir, Ann. Biomed. Eng. 2014, 42 (2), 445, doi.org / 10.1007 / sl0439-013-0902-7.

[0029] a) B. J. Nelson, S. Pane, Science 2023, 382 (6675), 1120, doi.org / doi: 10.1126 / science.adh3073; b) Y. Zhang, Y. Zhang, Y. Han, X. Gong, Micromachines 2022, 13 (5), 648; c) F. Soto, J. Wang, R. Ahmed, U. Demirci, Adv Sci (Weinh) 2020, 7 (21), 2002203, doi.org / 10.1002 / advs.202002203.

[0030] a) F. Baluska, M. Levin, Front Psychol ' 2016, 7, 902, doi.org / 10.3389 / fpsyg.2016.00902; b) C. I. Abramson, M. Levin, Commun Integr Biol 2021, 14 (1), 230, doi.org / 10.1080 / 19420889.2021.2005863.

[0031] C. G. Bell, R. Lowe, P. D. Adams, A. A. Baccarelli, S. Beck, J. T. Bell, B. C. Christensen, V. N. Gladyshev, B. T. Heijmans, S. Horvath, T. Ideker, J. J. Issa, K. T. Kelsey, R. E. Marioni, W. Reik, C. L. Relton, L. C. Schalkwyk, A. E. Teschendorff, W. Wagner, K. Zhang, V. K. Rakyan, Genome Biol 2019, 20 (1), 249, doi.org / 10.1186 / sl3059-019-1824-y.

[0032] C. Kerepesi, B. Zhang, S. G. Lee, A. Trapp, V. N. Gladyshev, Sci Adv 2021, 7 (26), doi.org / 10.1126 / sciadv.abg6082.

[0033] O. Krasnova, A. Kovaleva, A. Saveleva, K. Kulakova, O. Bystrova, M. Martynova, A. Domnina, J. Sopova, I. Neganova, Stem Cell Res Ther 2023, 14 (1), 373, doi.org / 10.1186 / S13287-023-03599-8.

[0034] a) F. Kuchling, K. Friston, G. Georgiev, M. Levin, Phys Life Rev 2020, 33, 88, doi.org / 10.1016 / j.plrev.2019.06.001; b) K. Friston, M. Levin, B. Sengupta, G. Pezzulo, J R Soc Interface 2015, 12 (105), doi.org / 10.1098 / rsif.2014.1383.

[0035] a) L. J. Bugaj, G. P. O'Donoghue, W. A. Lim, J Cell Biol 2017, 216 (1), 25, doi.org / 10.1083 / jcb.201612094; b) J. E. Toettcher, O. D. Weiner, W. A. Lim, Cell 2013, 155 (6), 1422, doi.org / 10.1016 / j. cell.2013.11.004; c) B. Zakirov, G. Charalambous, R. Thuret, I. M. Aspalter, K. Van-Vuuren, T. Mead, K. Harrington, E. R. Regan, S. P. Herbert, K. Bentley, Philos TransR Soc Bond B Biol Sci 2021, 376 (1821), 20190753, doi.org / 10.1098 / rstb.2019.0753.Atty. Dkt. No. 166118.01552

[0036] a) F. Baluska, A. S. Reber, W. B. Miller, Jr., Biosystems 2022, 218, 104694, doi.org / 10.1016 / j.biosystems.2022.104694; b) G. Longo, Found Sci 2018, 23 (3), 443, doi.org / 10.1007 / sl0699-017-9535-x; c) A. P. Moczek, Nature 2011, 473 (7345), 34, doi.org / 10.1038 / 473034a; d) T. Uller, A. P. Moczek, R. A. Watson, P. M. Brakefield, K. N. Laland, Genetics 2018, 209 (4), 949, doi.org / 10.1534 / genetics.118.300995; e) S. Kriegman, N. Cheney, J. Bongard, arXiv 2017, arXiv: 1711.07387 f) W. C. Ratcliff, J. D. Fankhauser, D. W. Rogers, D. Greig, M. Travisano, Nat Commun 2015, 6, 6102, doi.org / 10.1038 / ncomms7102.

[0037] S. Chen, Y. Zhou, Y. Chen, J. Gu, Bioinformatics 2018, 34 (17), i884, doi.org / 10.1093 / bioinformatics / bty560.

[0038] N. L. Bray, H. Pimentel, P. Melsted, L. Pachter, Nat Biotechnol 2016, 34 (5), 525, doi.org / 10.1038 / nbt.3519.

[0039] C. Soneson, M. I. Love, M. D. Robinson, FlOOORes 2015, 4, 1521, doi.org / 10.12688 / fl000research.7563.2.

[0040] M. D. Robinson, A. Oshiack, Genome Biol 2010, 11 (3), R25, doi.org / 10.1186 / gb-2010- 1 l-3-r25.

[0041] C. W. Law, Y. Chen, W. Shi, G. K. Smyth, Genome Biol 2014, 15 (2), R29, doi . org / 10.1186 / gb-2014-15 -2-r29.

[0042] M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, G. K. Smyth, Nucleic Acids Ac 2015, 43 (7), e47, doi.org / 10.1093 / nar / gkv007.

[0043] A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, J. P. Mesirov, Proc Natl Acad Sci USA 2005, 102 (43), 15545, doi.org / 10.1073 / pnas.0506580102.

[0044] Y. Benjamini, and Yosef Hochberg., Journal of the Royal Statistical Society: Series B (Methodological) 1995, doi.org / 10.1111 / j .2517-6161.1995.tb02031 x.

[0045] P. Jaccard, The New Phytologist 1912, doi.org / 10.1111 / j.l469-8137.1912.tb05611.x.

[0046] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Journal of Statistical Mechanics: Theory and Experiment 2008, 2008 (10), doi.org / 10.1088 / 1742- 5468 / 2008 / 10 / pl0008.

[0047] a) M. Milacic, D. Beavers, P. Conley, C. Gong, M. Gillespie, J. Griss, R. Haw, B. Jassal, L. Matthews, B. May, R. Petryszak, E. Ragueneau, K. Rothfels, C. Sevilla, V. Shamovsky, R. Stephan, K. Tiwari, T. Varusai, J. Weiser, A. Wright, G. Wu, L. Stein, H. Hermjakob, P.D'Eustachio, Nucleic Acids Res 2024, 52 (DI), D672, doi.org / 10.1093 / nar / gkadl 025; b) G. Yu, Q. Y. He, Mol Biosyst 2016, 12 (2), 477, doi.org / 10.1039 / c5mb00663e.

[0048] I. Yevshin, R. Sharipov, S. Kolmykov, Y. Kondrakhin, F. Kolpakov, Nucleic Acids Res 2019, 47 (Dl), D100, doi.org / 10.1093 / nar / gkyl 128.

[0049] T. Litman, W. D. Stein, Semin Oncol 2019, 46 (1), 3, doi . org / 10.1053 / j.seminoncol.2018.11.002.

[0050] T. Domazet-Loso, D. Tautz, BMC Biol 2010, 8, 66, doi.org / 10.1186 / 1741-7007-8-66.Atty. Dkt. No. 166118.01552

[0051] L. Yan, M. Yang, H. Guo, L. Yang, J. Wu, R. Li, P. Liu, Y. Lian, X. Zheng, J. Yan, J. Huang, M. Li, X. Wu, L. Wen, K. Lao, R. Li, J. Qiao, F. Tang, Nat Struct Mol Biol 2013, 20 (9), 1131, doi.org / 10.1038 / nsmb.2660.

[0052] L. Kolberg, U. Raudvere, I. Kuzmin, P. Adler, J. Vilo, H. Peterson, Nucleic Acids Res 2023, 51 (Wl), W207, doi.org / 10.1093 / nar / gkad347.

Claims

Atty. Dkt. No. 166118.01552CLAIMSWe Claim:

1. A method of detecting a composition that is effective for reducing a sign or symptom of aging, the method comprising:(a) contacting a test population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition; and(b) detecting a longevity parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

2. The method of claim 1, wherein the detected longevity parameter of the test population of multicellular constructs versus the detected longevity parameter of the control population of multicellular constructs indicates that the composition is effective for reducing a sign or symptom of aging, thereby detecting a composition that is effective for reducing a sign or symptom of aging.

3. The method of claim 1 or 2, wherein the test composition comprises a small molecule pharmaceutical, a biologic, a polynucleotide, an extracellular vesicle, other living cells, cellular constructs, or a nanoparticle.

4. The method of claim 3, wherein the biologic comprises an antibody.

5. The method of claim 1, wherein the control composition consists of a vehicle.

6. The method of claim 1, wherein the control composition does not comprise the test composition.

7. The method of claim 1, wherein the multicellular constructs comprise ciliated epithelial cells.

8. The method of claim 1, wherein the multicellular constructs comprise human cells.

9. The method of claim 1, wherein the ciliated epithelial cells are human airway epithelial cells.

10. The method of claim 1, wherein the longevity parameter is an average life span of the population of multicellular constructs.

11. The method of claim 10, wherein detecting the average life span of the multicellular constructs comprises monitoring the test population of multicellular constructs and the control population of multicellular constructs with time-lapse microscopy.Atty. Dkt. No. 166118.0155212. The method of claim 10 or 11 , wherein the detected average life span of the test population of multicellular constructs is greater than the control population of multicellular constructs.

13. The method of claim 1, wherein the longevity parameter comprises methylation clock analysis.

14. The method of claim 13, wherein the methylation clock analysis indicates that the test population of multicellular constructs has a reduced epigenetic age than the epigenetic age of the control population of multicellular constructs.

15. The method of claim 1, wherein the longevity parameter comprises an average area of the multicellular constructs in each of the test population of multicellular constructs and the control population of multicellular constructs.

16. The method of claim 15, wherein the average cross-sectional area of the test population of multicellular constructs is greater than the average cross-sectional area of the control population of multicellular constructs.

17. The method of claim 1, wherein the longevity parameter comprises an average time to disintegrate for the multicellular constructs in each of the test population of multicellular constructs and the control population of multicellular constructs.

18. The method of claim 17, wherein the time to disintegrate of the test population of multicellular constructs is greater than the control population of multicellular constructs.

19. The method of claim 1, wherein the method further comprises administering the test composition to a subject.

20. The method of claim 1, wherein the multicellular constructs are derived from a subject.

21. The method of claim 20, wherein the method further comprises administering the test composition to the subject from which the multicellular constructs are derived.

22. A method of detecting a pro-regenerative compound, the method comprising:(a) contacting a test population of multicellular constructs with a test composition and contacting a control population of multicellular constructs with a control composition;(b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

23. The method of claim 22, wherein the multicellular constructs in the test population and in the control population are damaged before step (a) of the method.Atty. Dkt. No. 166118.0155224. The method of claim 23, wherein the multicellular constructs are mechanically damaged, chemically damaged, optically damaged, or electromagnetically damaged.

25. A method of detecting a pro-regenerative compound, the method comprising:(a) contacting a test population of mechanically damaged multicellular constructs with a test composition and contacting a control population of mechanically damaged multicellular constructs with a control composition;(b) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

26. The method of claim 25, wherein the mechanically damaged multicellular constructs comprise multicellular constructs that are contacted with a needle.

27. A method of detecting a pro-regenerative compound, the method comprising:(a) mechanically damaging a plurality of multicellular constructs;(b) contacting a test population of the mechanically damaged multicellular constructs with a test composition and contacting a control population of the mechanically damaged multicellular constructs with a control composition;(c) detecting a regeneration parameter of the test population of multicellular constructs and of the control population of multicellular constructs.

28. The method of claim 27, wherein mechanically damaging a plurality of multicellular constructs comprises contacting each of the multicellular constructs with a needle.

29. The method of claim 23, wherein the test composition comprises a small molecule pharmaceutical, a biologic, a polynucleotide, or a nanoparticle.

30. The method of claim 29, wherein the biologic is an antibody.

31. The method of claim 23, wherein the control composition consists of a vehicle.

32. The method of claim 23, wherein the control composition does not comprise the test composition.

33. The method of claim 23, wherein mechanically damaging the multicellular constructs comprises contacting the multicellular constructs with a needle.

34. The method of claim 23, wherein the multicellular constructs comprise ciliated epithelial cells.

35. The method of claim 23, wherein the multicellular constructs comprise human cells.Atty. Dkt. No. 166118.0155236. The method of claim 35, wherein the ciliated epithelial cells are human airway epithelial cells.

37. The method of claim 23, wherein the regeneration parameter is an average life span of the multicellular constructs.

38. The method of claim 37, wherein detecting the life span of the multicellular constructs comprises monitoring the test population of multicellular constructs and the control population of multicellular constructs with time-lapse microscopy.

39. The method of claim 37 or 38, wherein the detected average life span of the test population of multicellular constructs is greater than the detected average life span of the control population of multicellular constructs.

40. The method of claim 23, wherein the regeneration parameter comprises methylation clock analysis.

41. The method of claim 40, wherein the methylation clock analysis indicates that the test population of multicellular constructs has a reduced epigenetic age than the epigenetic age of the control population of multicellular constructs.

42. The method of claim 23, wherein the regeneration parameter comprises an average area of the multicellular constructs in each of the test population of multicellular constructs and the control population of multicellular constructs.

43. The method of claim 42, wherein the average area of the test population of multicellular constructs is greater than the average area of the control population of multicellular constructs.

44. The method of claim 23, wherein the regeneration parameter comprises a time to disintegrate for each of the multicellular constructs in each of the test population of multicellular constructs and the control population of multicellular constructs.

45. The method of claim 44, wherein the time to disintegrate of the test population of multicellular constructs is greater than the control population of multicellular constructs.

46. The method of claim 23, wherein the method further comprises administering the test composition to a subject.

47. The method of claim 23, wherein the multicellular constructs are derived from a subject.

48. The method of claim 47, wherein the method further comprises administering the test composition to the subject from which the multicellular constructs were derived.Atty. Dkt. No. 166118.0155249. A population of multicellular constructs with improved longevity, wherein the multicellular constructs comprise ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct, wherein the mean initial area of population of multicellular constructs is greater than about 30,000 sq. microns, greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range.

50. The multicellular construct of claim 49, wherein the plurality of the ciliated epithelial cells are human cells.

51. The multicellular construct of claim 49 or 50, wherein the plurality of the ciliated epithelial cells are airway epithelial cells.

52. The multicellular construct of claim 49, wherein the plurality of the ciliated epithelial cells are human airway epithelial cells.

53. A method of generating a population of multicellular constructs with improved longevity, the method comprising separating, partitioning, enriching, or isolating a population of multicellular constructs with a mean initial area of greater than about 30,000 sq. microns, greater than about 35,000 sq. microns, or greater than about 40,000 sq. microns, greater than about 45,000 sq. microns, greater than about 30,000 to about 45,000 sq. microns, or greater than a value within 30,000 to 45,000 sq. microns, inclusive of the ends of the range, from an initial population of multicellular constructs to generate a population of multicellular constructs with improved longevity, wherein the multicellular constructs comprise ciliated epithelial cells, wherein a plurality of the ciliated epithelial cells of the construct are oriented with the cilia protruding out from the exterior surface of the construct.

54. The method of claim 53, wherein the plurality of the ciliated epithelial cells are human cells.

55. The method of claim 53 or 54, wherein the plurality of the ciliated epithelial cells are airway epithelial cells.

56. The method of claim 53, wherein the plurality of the ciliated epithelial cells are human airway epithelial cells.Atty. Dkt. No. 166118.0155257. A method of reducing the cellular age of a sample of cells, the method comprising causing an apical-basal inversion of the cells in vitro to reduce the cellular age of the cells, optionally, wherein the cells are ciliated epithelial cells.

58. The method of claim 57, wherein causing an apical-basal inversion comprises removing the epithelial cells from a medium comprising extracellular matrix.

59. The method of claim 58, wherein the extracellular matrix is a basement membrane extract.

60. The method of claim 59, wherein the basement membrane extract comprises Matrigel®.

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