Cell type specific RNAS in midbody remnants

Abstract

The present invention provides methods of using the RNA analysis to identify a cell type from which a midbody remnant (MBR) originated. The methods include isolating MBR, analyzing the RNA in the MBR to determine RNA transcript levels, and using the RNA transcript levels to identify a cell type from which the MBR originated. The method may be used to detect proliferative diseases including cancer by comparing RNA transcript levels to control levels.

Description

[0001] CELL TYPE SPECIFIC RNAS IN MIDBODY REMNANTS

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to U.S. Provisional Application No. 63 / 717,535 filed on November 7, 2024, the contents of which is incorporated by reference in its entirety.

[0004] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0005] This invention was made with government support under GM139695 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0006] FIELD OF INVENTION

[0007] The present disclosure relates to methods for analyzing midbody remnants, and more particularly to methods for identifying cell types of origin by analyzing RNA transcripts contained within midbody remnants isolated from biological samples.

[0008] BACKGROUND

[0009] The midbody (MB) is a protein and RNA-rich structure within the intercellular bridge assembled during mitosis at the overlapping plus ends of spindle microtubules, where it recruits and positions the abscission machinery that separates dividing cells. Long thought to be quickly internally degraded in daughter cell endo-lysosomes, recent studies revealed that a majority of MBs are released extracellularly as membrane-bound particles, or large extracellular vesicles, following bilateral abscission from nascent daughter cells. Released post-mitotic MB remnants (MBRs) are bound and tethered by neighboring cells, internalized, and can persist in recipient cells.

[0010] The functional importance of MBR signaling in the regulation of cell behavior, cell proliferation, architecture, and cell fate is only beginning to be understood. MBRs contain RNA and protein cargo that can be transferred to recipient cells, potentially influencing cellular functions and fate decisions. However, the composition and characteristics of MBR cargo from different cell types remain poorly characterized. Current methods for analyzing extracellular vesicles and their contents often lack the specificity to distinguish between different vesicle types or to identify their cellular origins. This limitation hampers the ability to understand the biological roles of different vesicle populations and their potential applications in diagnostics and therapeutics. In view of the foregoing, it would be desirable to have a better understanding of MBR function. SUMMARY

[0011] The present disclosure demonstrates that midbody remnants (MBRs) from different cell types cells contain distinct RNA transcripts within them, which can be used to detect the origin of MBRs found in biological samples. One aspect of the present invention comprises a method comprising (a) obtaining a biological sample from a subject, wherein the sample comprises a midbody remnant (MBR); (b) isolating the MBR from the sample, wherein the MBR comprises RNA; (c) analyzing the RNA in the MBR; and (d) using the RNA analysis to identify a cell type from which the MBR originated. In some embodiments, analyzing the RNA comprises sequencing, polymerase chain reaction, or real-time polymerase chain reaction to allow identification of the RNA transcripts found in the MBR and / or the amount of any RNA transcript in the MBR. In some embodiments, the biological sample comprises, plasma, serum, cerebral spinal fluid, urine, blood, saliva and tissue. In some embodiments, the RNA comprises transcripts from one or more of the following genes: PNRC1, IRF1, NFKBIZ, KLF4, KLF6, FOSB, FOS, JUN, NUPR1, NFKBIA, TEX14, PLK2, KIF23, ZFP36, IFRD1, ARL4A, SAT1, BTG2, BIRC3, CXCL2, AREG, ARC, Hl -4 and combinations thereof. In some embodiments, the RNA comprises transcripts from one or more of the following genes: LYPD3, ARC, CD3E, DLL4, FRMD1, FRZB, GRIA2, INPP5D, P2RY10, PTAFR, PTPRN, RABB, SH3RF2, SLC17A7, SYT5, DNAAF3, FAM166A, MR0H7, TULP2, MAFB, PRX. SCGB32A, FAM135B, EAF2, FOS, IRX1, KLF4, TP53INP2, FCN1, IRF8, LRRC25, LRRK1, RND1, IGF2, RASAL3, RGS2, RGSL1, ARPP21, HHLA1, KCNA2, NET1, SH3GL2, SNAI1, TRAK2, SCGB3A2, ACHE, CTNND2, and combinations thereof. In some embodiments, the transcripts from genes comprise RAB13, PRX, and RND1.

[0012] In some embodiments, the cell type is identified as a cancer cell when the quantity per MBR of transcripts from one or more of the genes is increased in the subject relative to a normal subject. In some embodiments, the RNA comprises transcripts from one or more of the following genes: MMRN1, HBB, LRCH2, NEFM, CACNG7, PCDH18, COL11A1, GRIN2B, RGS18, IRAG2, PCSK5, ARC, DPPA4, VCAN, FAT3, FAM166A, LIN28A, NTRK3, GABRB3, CAMK2A, RGSL1, SCUBE1, BCL11A, ANKDD1A, RABB, DCHS1, PTPRZ1, NRXN2, RND1, DYSF, SPRY4, HHLA1, JAKMIP2, FCN1, ATP1B2, SEMA5B, FAM135B, HMGA2, ADAMTS4, ALG1L2, TRIM71, NCALD, SHANK1, KLHL3, TRAK2, RGS2, UNC13A, DENND2A, P2RY10, TP531NP2 and combinations thereof. In some embodiments, the cell type is identified as a cancer cell when the quantity per MBR of transcripts from one or more of the genes is increased in the subject relative to a normal subject.

[0013] In some embodiments, the level of differentiation of the cell type is identified. In some embodiments, the cell type is identified as lacking differentiation by identification of particular RNA transcript presence or abundance in the MBRs or the cell type is identified as differentiated by identification of particular RNA transcript presence or abundance in the MBRs.

[0014] In some embodiments, the RNA comprises transcripts from one of more of the following genes: (a) HCN1, GRIN2B, ARC, CACNA1I, ADAM28, FOSB, RASD1, DISP3, EGR3, GALR1 and the cancer is a gynecological cancer or cervical cancer; or (b) PCSK5, RABIS, FLNC, TRAK2, SFRP2, ARRDC4, PRKCB, NET1, VC AN, HMGA2 and the cancer is a breast cancer; or (c) MUC16, STUM, NRSN1, PTPRN2, SHISAL2A, IGF2, C9orfl52, MUC5AC, MUC3A, ANXA2R and the cancer is an epithelial or eye cancer; or (d) PCSK5, MZB1, ESRI, HBB, ARC, TMEM255B, EYA2, ABCA12, ANKFN1, MUC5AC and the cancer is a liver cancer; or (e) LRRK1, SCGB3A2, AHNAK, GABRP, EGR3, TSHZ2, FOSL1, EGR1, PAPP A, BTG2 and the cells is an undifferentiated cell; or (f) IL10RA, DLX2, CNTN3, PTGER4, DIO3, IRF4, C2CD4B, DLL4, LEFTY1, DLX3 and the cells is an undifferentiated cell.

[0015] Another aspect of the present disclosure comprises a method for detecting a proliferative disease is a subject. The method comprises (a) obtaining a biological sample from the subject comprising a midbody remnant (MBR); (b) isolating the MBR from the sample and the RNA in the MBR; (c) analyzing the RNA in the MBR; and (d) using the RNA analysis to identify a cell type from which the MBR originated. In some embodiments, the methods include comparing the amount or level of RNA in the MBR to a level or quantity of the RNA in MBR from a control sample, wherein an increase in the level of RNA in the biological sample compared to that of the control sample is indicative of the proliferative disease in the subject.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Non-limiting embodiments of the present invention will be described by way of example with reference to the accompanying figures, which are schematic and are not intended to be drawn to scale.

[0018] Figure 1. Transcriptomic profiling of MBRs and matched cells across cell types. (A) Schematic overview of the study. mRNA was extracted from cells and their corresponding midbody remnants (MBRs), followed by RNA sequencing. Samples were categorized into cancer (HeLa, MCF7), stem (H9, iPSC), and differentiated (RPE, HepG2) cell types. (B) Principal component analysis (PCA) of all samples. MBRs and Cells correlate with cell type. (C) Heatmap of the top 1000 most expressed genes across all cell and MBR samples grouped by cell type using z-score average transcript per million (TPM). Matched cell and MBR samples show similarities with differences between the different cell types. (D) Correlation matrix of whole-transcriptome expression levels across all samples (Spearman method). Circle size and color indicate correlation strength.

[0019] Figure 2. Volcano plots showing differential gene expression and associated GO term enrichment of MBRs across six human cell types. (A-F), Volcano plots and Gene Ontology (GO) enrichment analyses comparing gene expression in MBRs versus corresponding cells, sorted by categories for cancer (A) HeLa, (B) MCF7; stem (C) H9, (D) iPSC; differentiated (E) RPE, and (F) HepG2 cell types. (A) HeLa cell MBRs are enriched in GO terms for development, differentiation and signaling. (B) MCF7 MBRs are enriched in GO terms for development and differentiation. (C) H9 MBRs are enriched in GO terms for development, differentiation and neurogenesis. (D) iPSC MBRs are enriched in GO terms for development and differentiation. (E) RPE MBRs are enriched in GO terms for development, neurogenesis and signaling. (F) HepG2 MBRs are enriched in GO terms for development, differentiation, and signaling. Volcano plots display log2(fold change) versus -logio(adjusted P value). GO enrichment analyses highlight the top 10 significantly enriched GO terms among MBR-enriched genes. Dot color represents - logio(adjusted P value), and dot size reflects the number of associated genes per GO term.

[0020] Figure 3. Comparison of MBR enriched genes from groups of cell types reveal common and unique genes. (A) Venn diagram displaying the overlap of MBR-enriched genes between cancer (HeLa and MCF7), cell types, highlighting both common and distinct expression patterns. Most highly expressed genes in cancer cells are shown in Table 1; most highly expressed genes in HeLa cells are shown in Table 4 and in MCF7 in Table 5. (B) GO enrichment of overlapping MBR-enriched genes within the cancer cell type group (HeLa and MCF7)Dot size and color reflect gene count and statistical significance. Cancer MBRs contain RNAs involved in development. (C) Venn diagram displaying the overlap of MBR-enriched genes between stem (H9 and iPSC) cell types, highlighting both common and distinct expression patterns. Most highly expressed genes in stem cells are shown in Table 2; most highly expressed genes in H9 cells are shown in Table 8, and in iPSC in Table 9. (D) GO enrichment of overlapping MBR-enriched genes within the stem (H9 and iPSC) cell type group. Dot size and color reflect gene count and statistical significance. Stem cell MBRs contain RNAs involved in development, differentiation and morphogenesis. (E) Venn diagram displaying the overlap of MBR-enriched genes between the differentiated (RPE and HepG2) cell types, highlighting both common and distinct expression patterns. Most highly expressed genes in differentiated cells are shown in Table 3; most highly expressed genes in RPE cells are shown in Table 7, in HepG2 cells in Table 6. (F) GO enrichment of overlapping MBR-enriched genes within the differentiated (RPE and HepG2) cell type group. Dot size and color reflect gene count and statistical significance. Differentiated cell MBRs contain RNAs involved in development, neurogenesis, neuronal differentiation, and morphogenesis. (G) Venn diagram showing the overlap of MBR-enriched genes between all cell types used in this study: cancer, stem, and differentiated groups. This analysis revealed that there are 3 genes (RND1, PRX and RAB I 3) that are common among all MBRs and are likely excellent pan-MBR markers. (H) Representative immunofluorescence images and quantification confirming RND1 localization to midbodies in HeLa (n = 37), iPSC (n = 46), and RPE (n = 79). RND1 and RACGAP colocalize in the dark zone of the midbody. The percent colocalization of RND1 with the midbody marker, RACGAP, is indicated. Scale bar represents 5 um. (I) Validation of RND1 presence in MBRs from HeLa (n = 16,800), iPSC (n = 9,795), and RPE (n = 11,987) cells. RND1 and RACGAP colocalize in isolated MBRs 100% of the time. The percent colocalization of RND1 with midbody remnant marker, RACGAP is indicated. Scale bar represents 5 um.

[0021] Figure 4. Ribosome profiling (Ribo-seq) reveals a majority of RNAs in isolated HeLa cell MBRs are NOT translated. (A) Venn diagram showing the overlap of genes enriched in RNA-seq and Ribo-seq datasets from isolated HeLa cell MBRs. 3147 RNAs (I) are not translated and are enriched in RNA-seq, 441 RNAs (II) are translated and enriched in both RNA-seq and Ribo-seq and 962 RNAs (III) are translated and are enriched in Ribo-seq. (B) Scatter plot showing the distribution of genes in log 2 fold change scale in RNA-seq (III), overlap (II), Ribo-seq (III) and not significant genes (IV). (C) GO term enrichment (biological process) of genes enriched in MBRs at the transcript level (RNA-seq) (I). These genes primarily function in development, morphogenesis and transport. (D) GO enrichment of genes commonly enriched in MBRs in both RNA-seq and Ribo-seq (II). These genes function in development and differentiation. (E) GO enrichment of genes uniquely enriched in MBRs in Ribo-seq (III). These genes correspond to organelle organization, anatomical structure morphogenesis and cytoskeletal organization. (F) Volcano plot showing the distribution of the Ribo-seq data 1403 RNAs were enriched and actively translating (II and III). (G) GO term analysis for RNA enriched transcripts detected in Ribo-seq data (II and III) These genes function in development, morphogenesis and cytoskeletal organization.

[0022] Figure 5. Comparison of MBR up-regulated genes in HeLa MBRs show common and distinct profiles and using these MBR signatures with machine learning can infer MBRs’ origin. (A) Comparison of MBR-enriched up- regulated genes and related proteins identified from Ribo-seq, RNA-Seq and Mass Spectrometry (MS) (this study) with 4 genes in common between all sets. Genes are shown in Tables 10-14. (B) MS GO term analysis of enriched proteins in HeLa MBRs. These genes correspond to organization, hemostasis and coagulation. (C) Scatter plot showing the distribution of up-enriched genes in MS and Ribo-eq. Genes with the largest Log2fc from each quadrant are labeled. I up-enriched in MS and down in Ribo-seq n=22, II up-enriched in both Ribo-seq and MS n=15III up-enriched in Ribo-seq and down in MS n=38, IV down genes in both Ribo-seq and MS n=385. (D) Scatter plot showing the distribution of up-enriched genes in MS and RNA-seq. Genes with the largest Log2fc from each quadrant are labeled. I up-enriched in MS and down in RNA-seq n=30, II up-enriched in both RNA-seq and MS n=19, III up-enriched in RNA-seq and down in MS n=27, IV down genes in both RNA-seq and MS n=541. (E) Schematic of the machine learning framework using a support vector machine (SVM) trained on cellular RNA-seq profiles to predict the cell type of origin for MBRs. In the future it may be possible to predict the cellular origin of MBRs from human liquid biopsies samples. (F) Accuracy of the SVM model in classifying MBRs by their parent cell type, based on RNA-seq features.

[0023] Figure 6. 26 Up expressed MBR genes sorted by GO terms in all cell types. Genes were enriched in signaling, development and differentiation. A (1) represents a yes for each cell type whereas a (0) represents not found in that MBR from that cell type. The majority of these genes were found in 5 out of the 6 cell types. We identified 3 genes, RABB, RND1, and PRX1, which were found in all MBRs from all cell types tested (Yellow, 6).

[0024] Figure 7. MB-enriched genes found in MBRs of all cell types. Previous work found 22 genes to be enriched along the intercellular bridge during mitosis in CHO cells (Park, 2023). Here, those 22 genes are shown grouped by function, along with the average expression (by TPM) of each within MBR samples of our six cell types. Genes that are found to be MBR up-expressed in our samples are highlighted. Note that some of the genes have low expression but still meet our cutoffs for differential expression (adjusted p-value 0.01, fold change 2 or 0.5). We have also added ARC as this was found in all MBRs and tested in Park, 2023.

[0025] Figure 8. Ribo-seq data. (A) Distribution of ribosome footprint length at the indicated conditions. (B) Fraction of the frame position for the 5' end of footprints (29 nt) in the indicated conditions. (C) Metagene plot of the relative ribosome footprint distribution (ribosome occupancy) along the 5' UTR, ORF, and 3' UTR. The length of the ORF was set to 1, whereas the lengths of the 5' UTR and 3' UTR were set to 0.5. (D) The relative distributions of the 5' UTR, ORF, and 3' UTR are shown. The mean (bar) and individual replicates (n = 3, points) are shown. (E) Volcano plots for MBR enrichment by Ribo-Seq. (F) Comparison of Ribo-seq and RNA-seq of MBR.

[0026] Figure 9. Mass Spec Analysis. (A) STRING network of proteins up-regulated in both Ribo-Seq and MS datasets. Protein-protein interaction network generated using STRING (version 12). Five of the eleven up-regulated proteins form a connected network. (B) STRING network of proteins up-regulated in both RNA-Seq and MS datasets. Protein-protein interaction network generated using STRING (version 12). Eight of the thirteen up-regulated proteins form a connected network. (C / D) Comparison of MBR- up and down regulated proteins identified from Ribo-seq and LC / MS (this study) with published MBR Mass spec data from Addi, et al, 2021 s: Gel (Addi, 2021), Ribo-seq (this study), LC / MS (this study), eFASP (Addi, 2021). The gel and eFASP correspond to Gel and eFASP preparation methods for mass spec used in (Addi, 2021). 2 up- regulated proteins are common, and 16 down-regulated proteins are common in all studies.

[0027] DETAILED DESCRIPTION

[0028] The midbody (MB) is a transient structure at the spindle midzone that is required for abscission the terminal stage of cell division. Long ignored as a vestigial remnant of cytokinesis, researchers have discovered that MBs are released post-abscission as large extracellular vesicles called midbody remnants (MBRs). Over the last 20 years, midbodies have been linked to a number of neurological disorders (microcephaly, Alzheimer’s, etc) and cancers.

[0029] The present disclosure demonstrates that midbody remnants (MBRs) from different cell types (e.g., cancer, stem and differentiated) cells contain both conserved and distinct RNA payloads, which can therefore be used to detect the origin of MBRs found in patient samples, or to focus / inform the use of MBRs in various applications. MBRs from cancer lines (HeLa / cervical and MCF7 / breast) and pluripotent stem cells (H9 / ESC and iPSC) and “fully” differentiated cells (RPE / retina and HepG2 / liver) were analyzed. The inventors found that the RNA signature of the MBRs from each source was distinctive, and that the RNA profile of the MBRs was different from that of the source cell itself (except for HepG2, which looked very similar to the cell). They looked for RNAs that were enriched in the MBRs (relative to their corresponding cells) and they found that three particular RNAs were identified in each MBR RNA profile (which could illuminate how the MBRs achieve their biological function). They also found that for each category, the two types tested shared some enriched RNAs and also all had distinct RNAs in their profile; for example, cancer cells share some RNAs (including signaling RNAs), and stem cell MBRs share some RNAs (particularly differentiation-related RNAs). Finally, the inventors show here for the first time that RNA is actively transcribed within the MBR.

[0030] One aspect of the present disclosure provides a method comprising (a) obtaining a biological sample from a subject, wherein the sample comprises a midbody remnant (MBR); (b) isolating the MBR from the sample, wherein the MBR comprises RNA; (c) analyzing the RNA in the MBR; and (d) using the RNA analysis to identify a cell type from which the MBR originated.

[0031] As used herein, a biological sample may be any biological fluid or tissue from which the isolation of MBR is desired. In some embodiment, the biological sample may comprise conditioned media, plasma, serum, cerebral spinal fluid, urine, blood, saliva or tissue. Conditioned media is the media in which a cell or ex-vivo tissue is growing. Conditioned media comprises the secretome of a cell or tissue in culture, including all proteins and vesicles secreted into a culture media. Conditioned media can be collected from a primary cell or a cell line. The cell or cell line may be engineered such that it expresses a target of interest, for example a cell line could be engineered to express a therapeutic agent or cargo. In some embodiments, the conditioned media will comprise a MBR which can be isolated with the methods described herein. To optimize isolation, the conditioned medium can be isolated from actively growing cells (i.e., cells actively undergoing mitosis and thus producing MBRs). The conditioned media may be harvested by decanting media from a culture of actively growing adherent cells or may be collected by centrifuging cells and collecting the cell-free supernatant (conditioned media) from the cells. The centrifugation at this step is sufficient to remove cells and cell debris but need not be an ultracentrifuge and can rely on a table-top centrifuge for example at 2000 x g to 5000 x g for 5-15 minutes. A biological sample may also comprise a biofluid, including, but not limited to blood, bile, bone marrow aspirate, breast milk, buffy coat, biopsy, cerebral spinal fluid (CSF), isolated cells, plasma, peripheral blood mononuclear cells, saliva, serum, sputum, stool, swabs (oral, nasal, vaginal fluids), cerebral spinal fluid, tissues, synovial fluid or urine. MBRs are shed into biological fluids from the tissues associated with these fluids. A biological sample may also comprise tissue, or any other sample comprising cells wherein the isolation of MBR is desired.

[0032] As used herein, a “subject” may refer to both mammals and non-mammals. The term “subject” does not denote a particular age or sex. In one embodiment, the subject is a human. The term “subject” may be used interchangeably with the terms “individual” and “patient”. A subject or “subject in need may” refer to a subject in need of treatment for a disease or disorder associated with proliferation or a subject in need of diagnosis for a disease or disorder. A biological sample may be obtained one or more times from the same subject to analyze the level or presence of MBR, or MBR related transcripts. A biological sample from a subject may also be obtained and analyzed to compare it to another sample- such as a sample from the same subject at a different time, from a non-diseased subject or to a reference or control sample.

[0033] Methods of isolating a MBR are known in the art. In some embodiments, the methods are disclosed in U.S. Patent Application Publication No. US2024 / 0409917, which is incorporated herein in its entirety. In some embodiments, the biological fluid is combined with a PEG solution. Combining denotes diluting the biological sample with a 2x to 5x concentration of the PEG solution to arrive at a final concentration of PEG in the combination of between 0.5% and 5% PEG (wt / v). As used herein, a polyethylene glycol (PEG), is a synthetic, hydrophilic and biocompatible polyether. The average molecular weight may be between 5,000g / mol to 7,000g / mol, such as PEG6000. These polymers are soluble in water as well as in many organic solvents, such as ethanol, acetonitrile, toluene, acetone, dichloromethane, hexane, and chloroform. PEG may aid in the precipitation of the MBR. PEG may be in a solution, wherein the PEG may be at a final concentration of at least 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 4% or 5%. In preferred embodiments, the PEG may have a final concentration of 1.0%-3%, suitably 1.5%. In some embodiments, the PEG may have a molecular weight in a range of about 5000g / mol to about 7000 / mol.

[0034] In some embodiments, the PEG solution may further comprise nanoparticles. The nanoparticles may be metal nanoparticles. Metal nanoparticles may comprise silver, gold, palladium, titanium, zinc, or copper. The metal nanoparticles may have magnetic properties, such as superparamagnetic iron oxide nanoparticles (SPION). The metal nanoparticles may be of varying size, for example from about 3 nm to about 50nm in size. The nanoparticles found to work well in the Examples were lOnm and 30nm in size. The surface of the nanoparticles may be coated, modified or functionalized to optimize the function of the nanoparticle. For example, the nanoparticle may be coated with PEG of different molecular weights. In some embodiments, gold or iron oxide nanoparticles with PEG 5000 may be used. In some embodiments the gold particle concentration ranges from 0.01% to 0.1% (v / v), suitably 0.02% (v / v).

[0035] In some embodiments, the PEG solution and biological sample are combined and incubated together. The combination may be incubated for at least 4hrs, 6hrs, 8hrs, lOhsr, 12hrs, 14hrs, 16hrs, 18hrs, 20hrs, 24hrs or any amount of time in between. The inventors have demonstrated that the incubation can be maintained for up to one week. The combination may be incubated at 10°C, 8°C, 6°C, 4°C or 2°C, or any temperature in-between. The incubation time and temperature may be adjusted accordingly, for example, a shorter incubation time may occur with a higher temperature.

[0036] In some embodiments the MBR are recovered from the combined PEG solution and biological sample by gravity collection on a slide or coverslip, via centrifugation or using a magnet if the iron oxide nanoparticles are included. The combination may be centrifuged at any speed in the range of about 5000xg, 4000xg, 3000xg, 2000xg, lOOOxg, 500xg, 400xg or 300xg or any speed in-between. The combination may be centrifuged at the defined speed for any time in a range of about 20 min, 15 min, lOmin, or 5min or any time in-between. The speed and time of the centrifugation may be adjusted accordingly for optimal recovery of MBR, for example, a slower centrifugation speed with increased time. Unlike prior methods, this method does not require the use of ultra-centrifugation at any point in the method. Thus, the centrifugation steps do not require speeds in excess of 50,000 rpm. For magnetic separation, magnetic separation techniques are well known in the art and can be used to collect the MBRs.

[0037] In some embodiments, the MBR can be labeled with a mitotic kinesin-like protein (MKLP1) affinity reagent. The MKLP1 may be used to aid in the recovery of the MBR, for example with the use of cell sorting techniques, including fluorescent activated cell sorting, magnetic cell sorting or adhesion (affinity)-based cell sorting. In some embodiments, other markers of MBR may be used, including, but not limited to ARC, ESCRT-III, CD9, CD63, CD81, HSP90, ALIX, TSG-101, RACGAP1, MgcRACGAPl, PLK1, AURK, CITK, ANNEXIN 11, TEX14, KLF4, FOS, JUN, ZFP36 or any other midbody antibodies known in the art. These markers may be used in isolation or combination with each other or additional extracellular vesicle markers such as CD9, CD63 and or CD81. In some embodiments, CD63 is used. MBR isolated with the methods described herein may comprise any proteins or nucleic acids from the cell from which it was formed. As described herein, the MBR isolated by the present methods may comprise ribosomal subunits, mitochondria and RNA. The MBR associated ribosomal subunits, including both small and large subunits, are translationally active and are capable of translating the mRNA to generate protein.

[0038] In some embodiments, the method further comprises detecting the presence of RNA in the MBR. RNA may be detected by any means known in the art, including but not limited to Northern blot analysis, nuclease protection assay, in situ hybridization, antibodies, probes, sequencingbased methods and / or reverse transcription-polymerase chain reaction (PCR), which includes realtime quantitative PCR. The RNA may be derived from any source, including, but not limited to pathogenic RNA such as viral RNA derived from a virus with an RNA based genome, or RNA associated with markers of cancer or sternness such as FOS / Jun or KLF4. The RNA may comprise any type of RNA, including, but not limited to coding RNA such as messenger RNA, non-coding RNA, such as ribosomal RNA, transfer RNA, small nuclear RNA, small nucleolar RNA, piwi- interacting RNA, microRNA or long noncoding RNA or circular RNA. The RNA may also comprise synthetic RNA such as guide RNA, CRISPR RNA, or tracer RNA. The RNA may also comprise an RNA expression profile, such that the expression, expression pattern or quantity of expression may be detected.

[0039] In some embodiments analyzing the RNA comprises determining the amount or level of RNA in the biological sample. The amount or level of RNA in a sample can be determined by any means known in the art. In some embodiments, the amount or level of a particular transcript is analyzed. In some embodiments, the amount or level of one or more, or combinations of transcripts is analyzed. The relative level of an RNA may also be measured and may be relative to a control or normalized level of an RNA in a reference subject. The RNA levels may be normalized to the level of an RNA found at relative equivalent levels in all MBRs as to indicate a relative amount of a tested RNA of interest.

[0040] In some embodiments, one or more RNA transcripts may be common to MBR and found in a MBR of any cell type or origin. In some embodiments, the RNA may comprise transcripts from one or more of the following genes: PNRC1, IRF1, NFKBIZ, KLF4, KLF6, FOSB, FOS, JUN, NUPR1, NFKBIA, TEX14, PLK2, KIF23, ZFP36, IFRD1, ARL4A, SAT1, BTG2, BIRC3, CXCL2, AREG, ARC, Hl -4 and combinations thereof. In some embodiments, the RNA may comprise a transcript of a gene in Figure 7.

[0041] In some embodiments, the RNA may comprise transcripts from one or more of the following genes: RND1, RABB, NET1, SH3GL2, SNAI1, P2RY10, LRRK1, DNAAF3, ARC, KLF4, FOS, RGS2, CD3E, DLL4, FRMD1, FRZB, GRIA2, PTAFR, PTPRN, SH3RF2, SYT5, PRX, TRAK2, SCGB3A2, FAM166A, MAFB, SCGB32A, TP53INP2, INPP5D, HHLA1, SLC17A7, LYPD3, EAF2, ACHE, CTNND2, ARPP21, KCNA2, FAM135B, MR0H7, TULP2, IRX1, FCN1, IRF8, LRRC25, IGF2, RASAL3, RGSL1 and combinations thereof. In some embodiments, the transcript may comprise any of those found in Figure 6. In some embodiments, the transcripts from genes comprise RAB , PRX, and RND1 or RABB, PRX, DLL and RND1 or a combination thereof. In some embodiments, the transcript is RND1. In some embodiments, at least one of RND1, RABB, and PRX may be used to identify MBR, then if the level of MBR is increased, additional RNAs may be measured to determine the cell type of the MBRs.

[0042] In some embodiments, the RNA detected within the MBR may be indicative of the cell type or origin of the MBR. The cell type or origin of the MBR is the cell type which generated the MBR. In some embodiments, the RNA transcripts can be analyzed to determine the cell type that produced the MBR. Some RNA transcripts may be common to more than one cell type, and some may be specific to the cell of origin. Combinations of RNAs may be used to determine the cell type of origin.

[0043] In some embodiments, the method described herein comprises analyzing the RNA in the MBR to identify a cell type form which the MBR originated. A cell type from which the MBR originated may comprise any cell which creates an MBR when it divides. In some embodiments, the cell type is a eukaryotic cell. Without limitation, cell types may comprise blood cells, central nervous system cells (including neurons and glial cells), epithelial cells, muscle cells, bone cells, adipose cells, hepatic cells, gastrointestinal cells, lung cells, kidney cells, pancreas cells, cardiac cells, retinal cells, reproductive system cells such as cervical, breast, or prostate cells, urinary cells, endothelial cells, epidermal or other skin cells, fibroblasts, stem cells, pluripotent cells, and cancer cells.

[0044] In some embodiments, a method described herein is used to identify the level of differentiation of a cell type. Cell differentiation is the process by which a cell changes or matures from an immature, unspecialized state to a mature, specialized state. MBR-enriched genes within stem cells may be related to organism development, cellular developmental processes, cellular differentiation, system development, organ development, anatomical structure and morphogenesis, cell development, regulation of development and cell differentiation or generation of neurons. By way of example, and not limitation, a cell type may be identified as lacking differentiation when the RNA comprises transcripts from one or more of the following genes: FOLR3, RAX, OSR1, SIX3, DLK1, GAT A3, ANKRD1, IGF2, DLX2, ATF7-NPFF, G0LGA6A, PHLDA2, ZNF703, SH3RF2, ALG1L2, KLF5, TMEM212, MPL, GDF15, PTGER4, TMEM249, LEFTY2, CDKN1A, DUSP10, COL3A1, COL 1 Al, ARHGAP19-SLIT1, PLK2, CHRNB4, FLNC, CDH6, IRF1, RASD1, NDRG1, CNTN3, LEFTY1, ID4, IRX1, PLAAT5, GALNT5, BHLHE40, ADRB2, APOLD1, SMC03, UTF1, ETFRF1, GADD45B, TNFSF9, SLC17A7, BMP4 and combinations thereof. In some embodiments, the transcripts may comprise any of those in Table

[0045] 3. MBR-enriched genes within differentiated cells may be related to multicellular organism development, system development, nervous system development, neurogenesis, neuron generation neuron differentiation, neuron development, neuron projection development or neuron morphogenesis. By way of example, and not limitation, a cell type may be identified as differentiated when the RNA comprises transcripts from one or more of the following genes: MUC16, HBB, DNAH8, TBX5, TNNT3, CD3E, MYH6, COLGA6L24, BPIFB3, OTOG, MUC5AC, MZB1, MAFB, MUC3A, OTOF, NRSN1, PGLYRP3, USH2A, EYA2, RABB, DU0X2, SLC4A1, FBN3, RND1, COL23A1, LCN15, HTR5A, GPR174, GRM4, PPFIA2, KCNN1, CD84, NRXN2, MUC4, HMCN2, MY016, DCHS1, DSCAM, FAM166A, COL20A1, KLF4, ABCA4, HBA1, FYB1, CFAP57, ELAVL3, ABCA6, WSCD2, RUNX1T1, RYRland combinations thereof. In some embodiments, the transcripts may comprise any of those in Table

[0046] 4.

[0047] In some embodiments, the RNA profile may be used to determine the cell type origin as well as identify the presence of cancerous cells. In some embodiments, the RNA may comprise: (a) (Hela) ARX, CFC1, THSD7B, POTEG, 0R2A1, VWC2L, TP53TG3E, HBB, ASB15, BP1FB6, HCN1, GRIN2B, ARC, CACNA1I, ADAM28, FOSB, RASD1, DISP3, EGR3, GALR1 and the cancer is a gynecological cancer or cervical cancer; or (b) (MCF7) P3R3URF-PIK3R3, COL11A1, MMRN1, CACNG7, GRIN2B, ARPP21, D0K5, MDFI, NEFM, MUC17, PCSK5, RABB, FLNC, TRAK2, SFRP2, ARRDC4, PRKCB, NET1, VCAN, HMGA2 and the cancer is a breast cancer; or (c) (RPE) CRYBA2, CRYBB1, HBB, BORCS7-ASMT, ARL2-SNX15, GHSR, MS4A14M MKX, F0XL3, CT47B1, MUC16, STUM, NRSN1, PTPRN2, SHISAL2A, IGF2, C9orfl52, MUC5AC, MUC3A, ANXA2R and the cancer is an epithelial or eye cancer; or (d) (HEPG2) MUC16, DNAH8, G0LGA6L1, GOLGA624, H3C11, FNDC1, SPTA1, SCL6A5, H0XB3, MYH2, PCSK5, MZB1, ESRI, HBB, ARC, TMEM255B, EYA2, ABCA12, ANKFN1, MUC5AC, and the cancer is a liver cancer; or (e) (H9) TFF1, SIX3, DLX5, NR2F2, DDX3Y, EN2, 0R13H1, BB0X1, TMPRSS11D, ECM2, LRRK1, SCGB3A2, AHNAK, GABRP, EGR3, TSHZ2, FOSL1, EGR1, PAPP A, BTG2, and the cells is an undifferentiated cell; or (f) (iPSC) MT3, PITX2, NEUR0G1, ITPKA, CH25H, GRP, CXCL11, RAX, ANTXRL, CHCT1, IL 1 ORA, DLX2, CNTN3, PTGER4, DIO3, IRF4, C2CD4B, DLL4, LEFTY1, DLX3, and the cells is an undifferentiated cell.

[0048] Another aspect of the present disclosure provides a method detecting a proliferative disease in a subject. In some embodiments, the method comprises obtaining a biological sample from a subject and isolating the midbody remnants (MBRs) with the method described herein and analyzing the RNA within the MBR. In some embodiments, the proliferative disease is cancer. MBR may be released from cancerous cells at a higher rate than from non-cancer cells because the cancer cells are actively dividing, and thus isolating MBR RNA from a set amount of serum or other biological or cellular sample from a subject over a particular amount of time and may be used to monitor for or as indicative of cancer. MBR released from cancer cells may comprise nucleic acids which comprise mutations commonly associated with cancer or transformed cells. MBR released from cancer cells may accumulate in or be taken up by other cells. MBR released from cancer cells may change the fate, identity or proliferative capacity of cells that take up the MBR and thus could be a source of metastases. MBR derived from cancer cells may also comprise unique markers only found in certain cancers and thus may be used for diagnosis, prognosis or surveillance for recurrence. MBR mRNA may also be analyzed to determine the cell type of origin of a cancer. In some embodiments, MKLP1 may be used to measure or count the MBR as a first step in analysis and prior to isolating or analyzing the RNA transcripts within the MBR.

[0049] In some embodiments, the cell type is identified as a cancer cell when the quantity per MBR of transcripts from one or more of the genes is increased in the subject relative to a control subject. The quantity of transcripts can be determined by any means known in the art. A control, or normal subject is one in which cancer is not suspected or not detected. In some embodiments, the level and or the RNA genes analyzed within the MBR may be indicative of a proliferative disease or of cancer. RNA found in cancerous cells may be from genes which participate in organism development or system development. In some embodiments, the RNA may comprise MMRN1, HBB, LRCH2, NEFM, CACNG7, PCDH18, COL11A1, GRIN2B, RGS18, IRAG2, PCSK5, ARC, DPPA4, VCAN, FAT3, FAM166A, LIN28A, NTRK3, GABRB3, CAMK2A, RGSL1, SCUBE1, BCL11A, ANKDD1A, RABB, DCHS1, PTPRZ1, NRXN2, RND1, DYSF, SPRY4, HHLA1, JAKMIP2, FCN1, ATP1B2, SEMA5B, FAM135B, HMGA2, ADAMTS4, ALG1L2, TRIM71, NCALD, SHANK1, KLHL3, TRAK2, RGS2, UNC13A, DENND2A, P2RY10, TP53INP2 and combinations thereof. In some embodiments, the RNA may comprise PNRC1, IRF1, NFKBIZ, KLF4, KLF6, FOSB, JUN, NUPR1, NFKBIA, TEX14, PLK2, KIF23, ZFP36, IFRD1, ARL4, SAT1, BIRC3, CXCL2, AREG, Hl-4 and combinations thereof. In some embodiments, the RNA may comprise LYPD3, ARC, CD3E, DLL4, FRMD1, FRZB, GRIA2, INPP5D, P2RY10, PTAFR, PTPRN, RABB, SH3RF2, SLC17A7, SYT5, DNAAF3, FAM166A, MR0H7, TULP2, MAFB, PRX. SCGB32A, FAM135B, EAF2, FOS, IRX1, KLF4, TP53INP2, FCN1, IRF8, LRRC25, LRRK1, RND1, IGF2, RASAL3, RGS2, RGSL1, ARPP21, HHLA1, KCNA2 and combinations thereof.

[0050] In some embodiments, the RNA comprises transcripts from one or more of the following genes: (a) ARX, CFC1, THSD7B, POTEG, 0R2A1, VWC2L, TP53TG3E, HBB, ASB15, BP1FB6, HCN1, GRIN2B, ARC, CACNA1I, ADAM28, FOSB, RASD1, DISP3, EGR3, GALR1, and the cancer is a gynecological cancer or cervical cancer; or (b) P3R3URF-PIK3R3, COL11 Al, MMRN1, CACNG7, GRIN2B, ARPP21, D0K5, MDFI, NEFM, MUC17, PCSK5, RABB, FLNC, TRAK2, SFRP2, ARRDC4, PRKCB, NET1, VCAN, HMGA2, and the cancer is a breast cancer; or (c) CRYBA2, CRYBB1, HBB, BORCS7-ASMT, ARL2-SNX15, GHSR, MS4A14M MKX, FOXL3, CT47B1, MUC16, STUM, NRSN1, PTPRN2, SHISAL2A, IGF2, C9orfl52, MUC5AC, MUC3A, ANXA2R, and the cancer is an epithelial or eye cancer; or (d) MUC16, DNAH8, G0LGA6L1, GOLGA624, H3C11, FNDC1, SPTA1, SCL6A5, H0XB3, MYH2, PCSK5, MZB1, ESRI, HBB, ARC, TMEM255B, EYA2, ABCA12, ANKFN1, MUC5AC, and the cancer is a liver cancer. In some embodiments, the transcript may comprise any of those from Tables 1- 10.

[0051] Table 1. List of top genes with greatest fold change found in cancer cell MBR

[0052]

[0053] Table 2. List of top genes with greatest fold change found in stem cell MBR

[0054] Table 3. List of top genes with greatest fold change found in differentiated cell MBR

[0055] Table 4. List of top genes with greatest fold change found in HeLa MBR

[0056] Table 5. List of top genes with greatest fold change found in MCF7 MBR

[0057] Table 6. List of top genes with greatest fold change found in HepG2 MBR

[0058]

[0059] Table 7. List of top genes with greatest fold change found in RPE MRB

[0060]

[0061] Table 8. List of top genes with greatest fold change found in H9 MBR

[0062] Table 9. List of top genes with greatest fold change found in iPSC MBR

[0063] Another aspect of the present disclosure provides a method of diagnosing a proliferative disease. In some embodiments, the method comprises measuring MBR RNA in a biological sample from a subject and comparing the level of RNA in the biological sample from a subject suspected of having a proliferative disease to the level of RNA in a control sample. In some embodiments, an increase in the level of RNA in the biological sample as compared to that in the control is indicative of the proliferative disease in the subject. A proliferative disease is a disease or condition in which cells grow and divide resulting in an increased number of cells beyond what is expected and where the increased number of cells contributes to disease pathogenesis. Without limitation proliferative diseases include cancer, atherosclerosis, rheumatoid arthritis, psoriasis, idiopathic pulmonary fibrosis, scleroderma and cirrhosis of the liver. In some embodiments, the biological sample comprises plasma, serum, cerebral spinal fluid, urine, blood, salvia and tissue. Without limitation cancer comprises bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, colon cancer, esophageal cancer, Gastric cancer, head & neck cancers, Hodgkin’s lymphoma, leukemia, liver cancer, lung cancer, melanoma, mesothelioma, multiple myeloma, myelodysplastic syndrome, non-hodgkin’s lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, rectal cancer, renal cancer, sarcoma, skin cancer, testicular cancer, thyroid cancer, uterine cancer and any other cancer or solid tumor.

[0064] As disclosed herein, the inventors also demonstrate that RNAs undergo active translation in MBRs using ribosome profding, also known as Ribo-seq. The inventors found 3,588 unique mRNA transcripts in MBRs, 1,403 of which exhibited significant enrichment. Of these, 441 transcripts were present in both the HeLa mRNA RNA-seq and Ribo-seq datasets, indicating a general overlap and positive correlation between the transcriptome and translate-tome within MBRs. Finally, the inventors also use mass-spectroscopy to investigate the proteomic profile, and compare it to the RNA-seq and Ribo-seq data. Genes found in common between Ribo-seq, mRNA seq and Mass Spec are shown in Table 10, the top 20 genes with the highest expression found in common between Rib-seq and mRNA seq are shown in Table 11 (sorted by the highest expression in Ribo-seq) and Table 12 (sorted by the highest expression in mRNA seq), genes found in common between Ribo-seq and mass spec are found in Table 13, and genes found in common between mRNA seq and mass spec are found in Table 14.

[0065] Table 10. Genes found in Ribo-seq, mRNA seq and Mass Spec (Fig. 5A)

[0066] Table 11. Genes found in Rib-seq and mRNA seq (Fig. 5 A): Genes with highest expression in Ribo-seq

[0067] Table 12. Genes found in Rib-seq and mRNA seq (Fig. 5A): Genes with highest expression in mRNA Table 13. Genes found in Ribo-seq and mass spec (Fig. 5A)

[0068] Table 14. Genes found in mRNA seq and mass spec (Fig. 5A)

[0069] Additional definitions The present disclosure is not limited to the specific details of construction, arrangement of components, or method steps set forth herein. The compositions and methods disclosed herein are capable of being made, practiced, used, carried out and / or formed in various ways that will be apparent to one of skill in the art in light of the disclosure that follows. The phraseology and terminology used herein is for the purpose of description only and should not be regarded as limiting to the scope of the claims. Ordinal indicators, such as first, second, and third, as used in the description and the claims to refer to various structures or method steps, are not meant to be construed to indicate any specific structures or steps, or any particular order or configuration to such structures or steps.

[0070] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to facilitate the disclosure and does not imply any limitation on the scope of the disclosure unless otherwise claimed. No language in the specification, and no structures shown in the drawings, should be construed as indicating that any non-claimed element is essential to the practice of the disclosed subject matter.

[0071] Unless otherwise specified or indicated by context, the terms “a”, “an”, and “the” mean “one or more.” For example, “a molecule” should be interpreted to mean “one or more molecules.”

[0072] 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 plus or minus <10% of the particular term and “substantially” and “significantly” will mean plus or minus >10% of the particular term.

[0073] 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 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.

[0074] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. For example, if a concentration range is stated as 1% to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., are expressly enumerated in this specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this disclosure. Use of the word “about” to describe a particular recited amount or range of amounts is meant to indicate that values very near to the recited amount are included in that amount, such as values that could or naturally would be accounted for due to manufacturing tolerances, instrument and human error in forming measurements, and the like. All percentages referring to amounts are by weight unless indicated otherwise.

[0075] 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. , “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.”

[0076] No admission is made that any reference, including any non-patent or patent document cited in this specification, constitutes prior art. In particular, it will be understood that, unless otherwise stated, reference to any document herein does not constitute an admission that any of these documents forms part of the common general knowledge in the art in the United States or in any other country. Any discussion of the references states what their authors assert, and the applicant reserves the right to challenge the accuracy and pertinence of any of the documents cited herein. All references cited herein are fully incorporated by reference, unless explicitly indicated otherwise. The present disclosure shall control in the event there are any disparities between any definitions and / or description found in the cited references.

[0077] Preferred aspects of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred aspects may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect a person having ordinary skill in the art to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

[0078] The following examples are meant only to be illustrative and are not meant as limitations on the scope of the invention or of the appended claims.

[0079] EXAMPLES

[0080] The following Examples are illustrative and should not be interpreted to limit the scope of the claimed subject matter.

[0081] Example 1.

[0082] Midbody remnants (MBRs) are large extracellular vesicles (EVs) that have emerged as intriguing signaling structures with far-reaching implications for cell biology, development, and disease (Patel et al., 2024; Kuriyama et al., 2025). Once considered mere debris from cell division, these post-cytokinetic organelles are now recognized as important signaling organelles and potential mediators of intercellular communication (Dionne et al., 2015; Jeffery et al., 2015; Lujan et al., 2017; Antanaviciute et al., 2018; Peterman and Prekeris, 2019; Farmer and Prekeris, 2022). The midbody forms during the final stages of cytokinesis, serving as a platform for the molecular machinery that orchestrates abscission - the physical separation of daughter cells. Following abscission, the central portion of the midbody persists as the MBR, a large extracellular vesicle, which can be retained by one of the daughter cells or released into the extracellular space(Crowell et al., 2014; Peterman et al., 2017; Peterman and Prekeris, 2019; Addi et al., 2020; McNeely and Dwyer, 2020, 2021; Park et al., 2023a).

[0083] MBRs are a unique class of large extracellular vesicles with a distinct molecular composition. These membrane-encapsulated structures are enriched in core cytokinetic proteins, reflecting their origin from the midbody during cell division (Rai et al., 2019; Park et al., 2023b; Patel et al., 2024). Mass spectrometry analysis of MBRs derived from colon cancer cells identified 2300 proteins, with 382 proteins unique to MBRs compared to other small and large extracellular vesicles (Suwakulsiri et al., 2023). The MBR proteome is molecularly distinct from exosomes and microparticles, highlighting the specialized nature of MBRs (Addi 2020, Suwakulsiri 2023) These variations in protein composition likely contribute to the diverse functional roles of MBRs in different cellular contexts, including stem cell maintenance, cancer progression, and intercellular signaling (Addi 2020, Suwakulsiri 2023,Kuriyama 2025).

[0084] Recent studies have revealed that MBRs are not simply passive byproducts of cell division, but rather dynamic structures with diverse fates and functions. The retention or release of MBRs appears to be cell-type specific and closely linked to cellular differentiation states (Chaigne et al., 2020). Stem cells, for instance, tend to release more MBRs compared to differentiated cells, suggesting a potential role in maintaining sternness (Ettinger et al., 2011; Chen et al., 2012). As stem cells undergo differentiation, there is a shift towards increased MBR retention, suggesting a link between MBR fate and cellular differentiation status. Conversely, cancer cells often accumulate MBRs intracellularly, which may contribute to their tumorigenic properties (Ettinger et al., 2011; Kuo et al., 2011). In neural stem cells (NSCs), MBR fate appears to be regulated in a stage-specific manner. During early development, when NSCs undergo symmetric proliferative divisions to expand the stem cell pool, MBRs are more frequently retained on daughter cells. This retention of MBRs in early proliferative divisions may contribute to the maintenance of sternness and influence subsequent cell fate decisions. These cell type-specific differences in MBR fate highlight the potential significance of MBRs in regulating cellular behavior, differentiation, and stem cell maintenance.

[0085] Intriguingly, MBRs contain a rich cargo of proteins and RNAs, including factors involved in cell fate determination and oncogenesis (Peterman et al., 2017; Addi et al., 2020; Park et al., 2023a). Equally interesting, is the unique ability to translate proteins inside the MBR (Park et al., 2023a). This molecular composition enables MBRs to potentially influence recipient cells upon internalization, raising the possibility of a novel mode of intercellular signaling(Park et al., 2023a). The discovery that MBRs can be engulfed by non-sister cells and potentially alter their behavior has opened up new avenues of research into their role in development, tissue homeostasis, and cancer progression (Crowell et al., 2014; Peterman et al., 2017).

[0086] To investigate the cell type-specific nature of RNA retention and release in MBRs between cancer and stem cells, we aimed to determine if the RNA content of MBRs varied across different cell types. This was motivated by previous findings suggesting that cell type-specific RNAs exist (Goering et al., 2023; Fishman et al., 2024) and that MBRs from cancer and stem cells appear to have different behaviors (Ettinger et al., 2011; Chaigne et al., 2020). This approach has the potential to reveal important insights into the role of MBRs in cell-to-cell communication and their impact on cancer progression, stem cell behavior, and other cellular processes (Ettinger et al., 2011; Peterman et al., 2017; Addi et al., 2020; Chaigne et al., 2020; Chaigne and Brunet, 2022). Our comprehensive analysis of MBR transcriptomes across multiple cell types aims to elucidate the molecular mechanisms underlying the diverse functions of MBRs and their potential as biomarkers or therapeutic targets in various physiological and pathological conditions.

[0087] Results:

[0088] Cell type-specific MBR RNAseq analysis: MBRs were isolated from asynchronous populations of various cell types using centrifugation methods developed by our lab (Patel et al., 2024). To examine the transcriptomic content of these MBRs, short-read RNA sequencing was carried out in triplicate on both MBRs and their corresponding cells of origin, also from asynchronous populations. This approach allowed for the identification of enriched RNA sets in MBRs compared to their parent cells. The analyzed populations comprised cancer cells (HeLa and MCF7), stem cells (H9 embryonic stem cells and induced pluripotent stem cells), and differentiated cells (RPE and HepG2). The purified MBRs displayed size heterogeneity, with diameters ranging from approximately 500 nm in stem cell-derived MBRs to 2.0 pm in cancer cell-derived MBRs. RNA-seq libraries were constructed from each cell and MBR sample (Fig. 1A), and subsequent gene enrichment analyses were performed. Because highly abundant mitochondrial and ribosomal transcripts tend to dominate sequencing data and can obscure detection of other transcripts, mitochondrial and ribosomal reads were removed from downstream analyses to enable more accurate identification of low-abundance and cell-type-specific RNAs.

[0089] Dimensionality reduction using principal component analysis (PCA), (Lever et al., 2017) revealed distinct clustering patterns: each MBR population grouped separately from its parent cell type, though typically within the same general region (Fig. IB). A single outlier, the HeLa MBR sample, was identified and excluded from further downstream analyses. Sample-specific transcriptomic profiles were further characterized by hierarchical clustering of the top 1000 enriched genes (Figure 1C), indicating broad similarity between each MBR group and the respective parent cell population.

[0090] To quantitatively assess these relationships, gene expression counts were normalized (e.g., transcripts per million or variance stabilizing transformation), and lowly expressed transcripts were excluded to minimize technical noise. Spearman correlation analysis was performed to measure the monotonic association between MBR and parent cell gene expression profiles, with correlation coefficients (p), statistical significance (p-values), and multiple testing correction reported. These analyses confirmed that, despite enrichment for select RNAs, MBRs generally recapitulate the gene expression patterns of their cell of origin (Fig. ID).

[0091] We prioritized and analyzed the most enriched transcripts for each cell type. This approach revealed that MBRs not only share a set of common mRNA species across all cell types, implicating core midbody-associated functions, but also contain a variety of unique transcripts that are specific to each cellular context — whether cancerous, stem, or differentiated.

[0092] These findings collectively indicate that MBRs harbor both conserved and cell-type- specific RNA populations. The presence of shared RNAs points to fundamental midbody functions, while the cell-type-specific RNAs suggest that MBRs may serve as indicators of the distinct physiological state and origin of their parental cells. This dual nature highlights the potential of MBR transcriptomic profiles as biomarkers for cellular identity and functional status.

[0093] Cell type specific MBR RNA enrichment analysis: To assess differential gene expression in our cell-type-specific MBR RNA-seq datasets, we employed volcano plots to visualize transcriptomic changes between isolated MBRs and their corresponding whole-cell counterparts for each cell type (Fig 2). Raw sequencing read counts were normalized using the DESeq2 package (Love 2014), which corrects for differences in library size and RNA composition. For each gene, the log2 fold change was calculated to represent the magnitude of expression changes between MBRs and whole cells within the same cell type. Statistical significance was determined using DESeq2’s Wald test, and resulting p-values were adjusted for multiple comparisons using the Benj ami ni -Hochberg procedure to control the false discovery rate (FDR).

[0094] Volcano plots were generated for each cell type by plotting log2FC against the negative loglO of the adjusted p-value for all genes. Genes meeting the criteria of adjusted p-value < 0.05 and |log2FC| > 1 were deemed significantly differentially expressed and visually highlighted. This analytical workflow enabled rapid identification of genes exhibiting both statistically significant and biologically meaningful expression differences between MBRs and whole cells.

[0095] To further interpret the biological implications of these expression changes, we performed Gene Ontology (GO) enrichment analysis for the differentially expressed genes (DEGs) identified in each cell type (Ashbumer et al., 2000). We used the clusterProfiler R package (version 4.12.6) to perform GO enrichment analysis. We focused on the GO category Biological Process (BP), limiting it to terms with a minimum gene set size of 2 and a maximum of 5000. The tool calculated the enrichment of GO terms within our gene lists compared to the background of all human genes. We considered GO terms with a Benjamini -Hochberg adjusted p-value < 0.0001 as significantly enriched. The resulting GO terms were then clustered based on their semantic similarity to identify major functional themes and reduced using clusterProfiler’s simplify function with a term similarity cutoff of 0.9. We visualized these results using bar plots showing the -loglO (adjusted p-value) for the top enriched GO terms in each category sorted by adjusted p-value. This comprehensive GO analysis allowed us to gain insights into the potential biological roles, and subcellular localizations of the proteins encoded by the differentially expressed genes in MBRs across different cell types. Notably, each MBR exhibited enrichment for distinct sets of GO terms, indicating that the transcriptomic landscape and potential functional roles of MBRs are unique for each cell type.

[0096] In HeLa-derived MBRs, mRNAs involved in cell cycle regulation, mitosis, and oncogenesis were predominantly enriched, reflecting the highly proliferative and transformed nature of this cell line (Fig. 2A). MCF7 MBRs exhibited a distinct profde, with significant enrichment for transcripts related to estrogen signaling, cellular proliferation, and migration — pathways central to breast cancer biology (Fig. 2B). By contrast, MBRs from pluripotent cell populations, including H9 embryonic stem cells (Fig. 2C) and induced pluripotent stem cells (iPSCs) (Fig. 2D), were enriched for mRNAs encoding core pluripotency and self-renewal regulators such as OCT4, SOX2, and NANOG, alongside transcripts governing early developmental processes.

[0097] For retinal pigment epithelial (RPE)-derived MBRs (Fig. 2E), the transcriptome was marked by higher abundance of mRNAs implicated in epithelial integrity, cell-cell adhesion, and cellular defense against oxidative stress, consistent with the specialized functional roles of pigment epithelial cells. HepG2 MBRs were primarily populated with transcripts associated with chromosome organization, DNA repair, and cell cycle control, while showing relative depletion of metabolic and immune-related mRNAs — distinguishing the MBR transcriptome from that of non-transformed hepatic cells (Fig. 2F).

[0098] Collectively, these results demonstrate that MBRs function as selective repositories for mRNA species, with transcript cargo varying systematically according to the identity and physiological state of the parent cell. This cell-type specificity in MBRRNA composition suggests potential roles in modulating intercellular signaling, influencing cell fate decisions, and contributing to disease processes such as cancer progression.

[0099] Cell-type MBR comparisons reveal common and unique gene sets:

[0100] Analysis of RNA-seq data demonstrated that MBRs isolated from cancer cells, stem cells, and differentiated cells exhibit distinctly different transcriptomic profiles (Fig. 3A, C, E). Notably, a subset of transcripts was found to be uniquely enriched or exclusively present in MBRs from cancer and stem cells, distinguishing them from those derived from differentiated cells. MBRs from cancer cells and stem cells were predominantly enriched in mRNAs related to pluripotency, cell cycle regulation, and signaling pathways crucial for stem cell maintenance and self-renewal (Fig. 3B, D) In contrast, MBRs originating from differentiated cells contained higher levels of transcripts associated with cellular differentiation, cytoskeletal organization, and metabolic pathways characteristic of committed, non-stem-like states (Fig. 3F). Furthermore, several transcripts involved in signal transduction and intercellular communication displayed differential abundance between these groups, suggesting that MBRs may influence or reflect cellular identity through their selective RNA cargo (Fig. 3). Collectively, these findings highlight the functional and molecular heterogeneity of MBRs across different cellular states and support the hypothesis that MBR-associated RNAs contribute to the maintenance of sternness in proliferative cells or promote differentiation in more specialized cell types. This underscores the potential role of MBRs as regulators of cell fate and identity within diverse cellular contexts.

[0101] Core RNAs found in all MBRs may serve as universal MBR biomarkers: To identify genes that are in common with all MBRs analyzed, we compared the MBR transcriptomes from Cancer, Stem and Differentiated gene sets. MBRs show a high enrichment of signaling and development-associated transcripts (Fig. 6). Here, we identified a core set of about 3 RNAs that were common between cancer, stem and differentiated cell types (Fig. 6), that play a role in development, differentiation and neuronal development (Fig. 3B, D, F). Further analysis identified 26 mRNAs significantly enriched in 5 out of 6 cell type specific MBRs compared to whole-cell RNA samples. The enrichment of these diverse RNA classes in MBRs suggests their potential roles in intercellular communication and regulation of cellular processes like development and differentiation (Fig. 6).

[0102] Comparative analysis of MBRs isolated from six distinct cell types identified a set of consistently enriched transcripts, suggesting their potential utility as universal MBR biomarkers. Among these, three transcripts — RND 1 , PRX, and RAB 13 — were found to be present in the MBRs of all cell types analyzed (Fig. 3G, Fig. 6). These genes are implicated in key cellular functions, including signaling, cytoskeletal function, and membrane trafficking. To experimentally validate MBR localization, we focused on RND1 as a representative marker and examined its distribution in HeLa, induced pluripotent stem cells (iPSCs), and retinal pigment epithelial (RPE) cells. Immunofluorescence microscopy revealed that RND1 protein robustly co-localizes with the established midbody marker RacGAP at the midbody structure, with 100% co-localization observed in HeLa and iPSC midbodies, and 92.4% co-localization in RPE cell midbodies. Interestingly, for all iPSC midbodies there was a high concentration of tubulin at the dark zone not observed in HeLa or RPE midbodies. Furthermore, in all tested cell types, RND1 protein showed complete (100%) co-localization with RacGAP in isolated MBRs, confirming its reliable presence in this structure (Fig. 3H, 31). These findings support the conclusion that both RND1 RNA and protein are selectively localized to midbodies and MBRs, underscoring its potential as a conserved molecular hallmark of this structure across diverse cell types.

[0103] RND1 encodes a small signaling G protein (~21 kDa) belonging to the Rnd subgroup of the Rho family GTPases. Unlike typical Rho GTPases, RND1 is constitutively active and bound to GTP, with its activity regulated by expression, localization, and phosphorylation rather than GDP / GTP cycling. In signaling, RND1 plays crucial roles in regulating actin cytoskeleton organization, cell adhesion, and migration. It interacts with various partners, including plexins, to modulate semaphorin-mediated signaling in axon guidance and neuronal polarity. RND1 is also involved in FGF signaling during neurite development in PC12 cells. In development, RND1 contributes to the regulation of neurite extension and growth cone collapse, depending on its specific interactions. Additionally, RND1 has been implicated in innate immunity, providing protection against viral and bacterial infections through NF-KB activation and cytokine production. RND1 mutations can have significant consequences on cellular function and organism health. These mutations can disrupt epithelial adhesion and polarity, potentially leading to neoplastic conversion and tumor formation, especially when combined with other genetic alterations. RND1 deficiency can enhance metastasis and alter cell cycle progression, often resulting in premature senescence. Additionally, RND1 mutations can impair innate immunity, compromising the body's defense against pathogens. In embryonic development, severe RND1 deficiency can lead to gastrulation failure and, in some cases, death. These effects underscore RNDl's crucial role in tumor suppression, innate immunity, and developmental processes.

[0104] PRX (Periaxin) encodes an antioxidant enzyme that plays important roles in cell signaling and redox regulation. PRX can act as a sensor and barrier to the activation of mitogen-activated protein kinase (MAPK) signaling pathways and has been shown to primarily maintain and stabilize peripheral nerve myelin. Mutations in the PRX gene have been associated with early- onset demyelinating Charcot-Mari e-Tooth disease and severe sensory loss. PRX also interacts directly with PTEN. PRX plays a crucial role in regulating PTEN through redox mechanisms, with significant implications for cellular signaling and disease processes. This protective function preserves and promotes PTEN's tumor-suppressive activity.

[0105] RABIS, a small GTPase of the Rab family, plays crucial roles in signaling and development by regulating membrane trafficking and protein localization. It controls the assembly and function of tight junctions, influencing epithelial and endothelial barrier integrity. In development, RABIS participates in spermatogenesis by regulating ectoplasmic specialization dynamics in the testis. In cancer, RAB 13 is implicated in tumor progression and metastasis. It regulates the secretion of small extracellular vesicles in colorectal cancer cells, potentially influencing tumor microenvironment interactions. RABIS is highly expressed in breast cancer stem cells and supports their sternness, turn ori genesis, and chemoresistance by mediating tumorstroma cross-talk. RABIS expression varies across cancer types and stages, suggesting its potential as a biomarker or therapeutic vehicle.

[0106] Additionally, mRNAs encoding proteins involved in mitochondria and ribosomal function were found to be enriched in MBRs across various cell types, suggesting the importance of translation and energy required for protein synthesis in MBRs. The consistent presence of these transcripts in MBRs from different cellular origins highlights their potential in therapeutics. The three biomarkers, RND1, PRX and RAB 13 can serve as universal biomarkers for MBRs, which could be valuable for detecting and studying these structures in various biological contexts.

[0107] Lastly, we identified ARC as highly expressed in 5 out of the 6 cell types (Fig. 6), as we identified previously in CHO midbodies (Fig. 7) (Park 2023). ARC plays a crucial role in both MBRs and neuronal EVs. In MBRs, ARC is essential for loading RNA into the midbody and midbody remnant, facilitating the transfer of genetic information between cells. ARC forms viruslike capsids that are released in EVs, enabling cell-to-cell signaling. In neurons, ARC-containing EVs mediate the transfer of ARC mRNA and protein to neighboring cells, influencing synaptic plasticity. ARC is also involved in the biogenesis of EVs during long-term potentiation, recruiting IRSp53 to dendrites and facilitating capsid assembly and release. This mechanism allows for intercellular synaptic plasticity and may play a role in memory consolidation. The dual function of ARC in MB Rs and neuronal EVs highlights its importance in cellular communication and information transfer across various cell types.

[0108] Ribo-Seq analysis of HeLa cell MBRs reveals actively translated RNAs:

[0109] To profile RNAs undergoing active translation in MBRs, we performed Ribosome profiling (Ribo-seq) on both parental HeLa (CCL2) cells and their corresponding isolated MBRs, using the Thor-Ribo-Seq protocol. We observed a lower abundance of the canonical footprint length (approximately 30 nucleotides) in the MBR samples (Figure 8), but they clearly displayed the three-nucleotide periodicity of the 30 nt footprints (Figure 8B). Furthermore, most of the reads were mapped to the CDS region, which validates the quality of the Ribo-seq data for downstream analysis(Figure 8C). HeLa cells were cultured under standard conditions and harvested at appropriate confluency for MBR collection. MBRs were isolated from the culture medium by differential centrifugation to ensure the enrichment of fractions corresponding to the expected size range (1.0-2.0 pm) and to minimize contamination from other cellular debris or organelles. Both whole cells and isolated MBRs were treated with translation inhibitors (cycloheximide and chloramphenicol) to arrest ribosomes on mRNAs at all isolation stages prior to lysis. Samples were then subjected to nuclease digestion to generate ribosome-protected mRNA fragments (REFs). The ribosome-protected fragments were purified, size-selected, and converted into cDNA libraries. High-throughput sequencing was performed, followed by rigorous bioinformatic processing, including read trimming, alignment to the human genome, and filtering to remove contaminating rRNA / tRNA reads.

[0110] We detected a total of 3,588 unique mRNA transcripts in MBRs, 1,403 of which exhibited significant enrichment.. Of these, 441 transcripts were present in both the HeLa mRNA RNA-seq and Ribo-seq datasets (Fig. 4A), indicating a general overlap between the transcriptome and translatome within MBRs. Furthermore, a positive correlation was observed between the transcriptome and the translatome (Fig. 4B II). This supports the view that transcripts present in high quantities at the RNA level are more likely to be translated in MBRs, in a manner similar to cytosolic translation. HeLa mRNA transcripts in MBRs that were not translated (Fig. 4A I, 4C) were associated with organism and system development, morphogenesis and ion transport whereas those that were translated (Fig 4A II, 4D) were associated to a lesser extent with organism and system development and had transcripts associated with cellular development processes and cell differentiation. The transcripts present in Ribo-seq but not RNA-seq (Fig. 4A III, 4E) were associated with organelle and cytoskeletal organization. Comparing this to the GO analysis on all Ribo-seq transcripts (Fig 4F, 4G) the association with development, differentiation and organization are again present indicating that actively translated mRNAs within the HeLa MBR may perform important roles in cellular communication.

[0111] These integrated datasets yield critical insights into the regulatory logic and potential signaling roles of MBR-localized mRNAs in the context of HeLa cell division, midbody inheritance, and post-mitotic cellular processes. The identification of actively translated mRNAs specific to MBRs also provides candidate transcripts for functional follow-up studies to investigate their roles in midbody biology and cellular communication.

[0112] Comparison of RNA-seq and Ribo-seq to proteomics data identifies common and unique transcripts:

[0113] To increase our knowledge of the HeLa MBRs we also carried out mass spectrometry (MS) to compare the proteomic profile to the MBR RNA-seq and Ribo-seq data. There werejust 4 genes in common with all 3 datasets (Fig. 5A). MS GO terms related to organization, homeostasis and coagulation (Fig 5B). Comparing up-enriched genes between Ribo-seq and MS gave 15 transcripts in common with APOB the most enriched (Fig. 5C). Using STRING, 5 out of the 11 proteins up- regulated in both the Ribo-Seq and MS datasets were found to form a connected interaction network (Fig. 9A). Gene Ontology analysis of the full 11-protein set revealed enrichment in lysosomal lumen components, ECM-receptor interactions, and integrin-mediated cell-surface interactions. Comparing the up-enriched genes between RNA-seq and MS gave 19 transcripts in common with F5 being the most enriched. Also using STRING, 8 out of 13 proteins upregulated in both mRNA-Seq and MS datasets were found to form a connected interaction network (Fig. 9B) Gene Ontology analysis shows enrichment in endoplasmic reticulum lumen, extracellular space, and ECM-receptor interaction.

[0114] Comparing our datasets with published mass spec datasets for the HeLa cell Flemmingsome or MBRs (Addi et al., 2020), revealed both similarities and differences in their molecular compositions. Both datasets show that MBRs are enriched in components related to cytokinesis, cell fate determination, and oncogenesis (Addi et al., 2020). However, there are notable differences between the two datasets. The transcriptome analysis revealed a high enrichment of mitochondrial transcripts and ribosomal transcripts in MBRs, which were not prominently reported in proteomics studies. Additionally, the RNAseq data identified several transcripts highly enriched in MBRs compared to other extracellular vesicles and cell lysates, a finding not reflected in proteomic analyses. These discrepancies highlight the importance of post- transcriptional regulation in MBRs and underscore the complementary nature of transcriptomic and proteomic approaches in understanding MBR composition and function.

[0115] Using Machine Learning to predict cell of origin from MBRs from cell culture to liquid biopsy: Recent advances in single-cell and EV transcriptomics have opened the door to leveraging machine learning (ML) for tracing the cell of origin based on RNAseq data — including from specialized EVs such as MBRs. These efforts aim to develop classifiers that can accurately predict the cellular (and tissue) source of MBRs isolated from patient samples, based on transcriptomic profiles first established from well-defined cell lines or experimental models.

[0116] The foundational step in such approaches involves profiling the RNA content of MBRs from various parent cell types using high-throughput RNA sequencing. By building a reference expression matrix of cell-type- or tissue-specific genes — and training on these profiles using established or custom ML pipelines — researchers can develop robust algorithms that distinguish between possible cellular origins. Feature selection techniques, such as the tissue-specific score (TSS) strategy, maximum absolute deviation (MAD), or iterative permutation importance, help narrow down the most informative genes for accurate classification (Zhou et al., 2020; Chen et al., 2024; Kugeratski et al., 2021).

[0117] A variety of ML models — including support vector regression (SVR), random forests, logistic regression, and deep learning architectures — have been deployed to address the deconvolution and classification challenge. Notably, tools like the EV-origin pipeline have shown that deconvolution models trained on bulk or single-cell RNA-seq datasets can reliably estimate the proportional contribution of different tissue or cell types to the RNA cargo found in EVs (Zhou et al., 2020; Jeppesen et al., 2023; Park et al., 2023). These models excel at handling the heterogeneity and mixed-cell signal inherent to patient-derived extracellular vesicle samples. Rigorous validation, including simulated mixtures and independent patient datasets, is crucial to ensure translatability and robustness for clinical applications.

[0118] When applying these approaches to MBRs from patients, ML classifiers trained on curated and annotated MBR RNAseq datasets can be used to assign single MBRs, or pools of MBRs, to their likely tissue or cellular origin. For example, highly enriched transcripts for sternness, proliferation, or lineage-specific markers — in combination with ribosomal and translation machinery profiles — can serve as powerful discriminators (Park et al., 2023; Addi et al., 2020). Integrative models in the pipeline can incorporate not just the RNAseq data, but also auxiliary clinical or biochemical features to improve diagnostic performance (Patel et al., 2024; Chen et al., 2024).

[0119] Emerging studies demonstrate the clinical potential of this approach. RNAseq features from EVs, analyzed with random forest or SVM classifiers, have already achieved high accuracy (AUC >0.95) for origin prediction in cancer diagnostics using blood-derived EVs (Zhou et al., 2020; Gutkin et al., 2016; Skog et al., 2008). As datasets from patient-derived MBRs expand, these machine learning strategies offer a path to noninvasively identify tumor or tissue sources from liquid biopsies — paving the way for sensitive, precise tools for diagnostic and prognostic applications in personalized medicine.

[0120] To validate the potential of identifying the cell of origin within our own experiments, we conducted a machine learning classification assessment using off-the-shelf technology. Given the abundance of publicly available cell and tissue RNA-seq data, but still limited availability of MBR data, we aimed to identify the cell of origin for our MBR samples by training a model on only the cell samples and no additional specialized feature selection or tuning methods. In this way, we can assess the feasibility to leverage vast public repositories of data to identify the MBRs cell of origin without necessarily collecting vast quantities of MBR specific sequencing.

[0121] We first separated our TPM data into MBR and cell samples. We included the same 19,240 genes used in all other analyses within this work as features. Next, we standardized the cell samples, subtracting the mean and scaling features to unit variance, and fit a linear SVM classifier on the resulting data. Finally, we standardized the MBR samples and applied the fit classifier to predict cell type for each of our MBR samples (Fig. 5E). Figure 5F shows a confusion matrix identifying true and predicted labels for each sample. Numbers along the diagonal represent correct classifications. Our results show 65% cell type classification accuracy using off-the-shelf technology and minimal data, suggesting feasibility of classifying MBRs to their cell type of origin using only cell sequencing data. We use Scikit-leam for this assessment.

[0122] Technical considerations: In conducting our RNAseq analysis of MBRs, we encountered several technical challenges that required careful consideration and mitigation strategies. One primary concern was the low RNA yield from MBRs, given their small size and limited quantity. To address this, we employed a modified RNA extraction protocol optimized for small samples and used low-input RNA sequencing libraries. We also implemented rigorous quality control measures, including RNA integrity number (RIN) assessment, to ensure the reliability of our sequencing data. To ensure sufficient depth for performing differential expression, we also repeated samples that reached fewer than 5 million mapped reads. Finally, we performed principal component analysis (PCA) to qualitatively identify outliers. We removed samples determined to be outliers from downstream analysis. By addressing these technical challenges, we were able to generate high-quality, reliable RNAseq data that provided valuable insights into the transcriptomic landscape of MBRs.

[0123] Discussion

[0124] Our comprehensive analysis of MBR transcriptomes across multiple cell types has revealed novel insights into the composition and potential functions of these enigmatic organelles. The identification of both common and cell-type specific transcripts in MBRs provides evidence for their specialized roles in different cellular contexts. For instance, the enrichment of 186 genes in cancer cell-derived MBRs suggests a potential mechanism for the transfer of oncogenic information between cells. Similarly, the presence of transcripts involved in pluripotency and differentiation in stem cell-derived MBRs points to a possible role in cell fate determination. Lastly, the identification of more tissue specific transcripts in RPE and HepG2 cells suggested that these cell types are differentiated than the other cell types we assayed.

[0125] Our analysis also revealed several consistently present transcripts across different cell types, which could serve as potential universal biomarkers for MBRs. These include mRNAs encoding proteins involved in signaling, development and differentiation. The enrichment of these transcripts in MBRs suggests a specific targeting mechanism rather than passive accumulation.

[0126] The functional implications of cell type-specific MBRs are diverse and significant. In stem cells, MBRs play a crucial role in maintaining sternness and regulating differentiation. Stem cells tend to release more MBRs compared to cancer cells, and an increase in MBR release is observed during differentiation. This suggests that MBR retention may be associated with maintaining an undifferentiated state, while release could facilitate differentiation processes. In cancer progression, MBRs have been implicated in promoting tumorigenic behavior and enhancing cell proliferation. The accumulation of MBRs in cancer stem cells may contribute to their aggressive nature and ability to self-renew. Released MBRs function as signaling organelles which can be internalized by other cells. These MBRs stimulate cell proliferation and contain various signaling molecules, including MEK1 / 2 / 3, IQGAP1, PAK1 / 2, and RAC1, which can activate downstream pathways in recipient cells(REF). Additionally, MBRs carry mRNAs encoding proteins involved in cell fate determination, oncogenesis, and pluripotency, further emphasizing their potential role in intercellular communication and cellular reprogramming.

[0127] The comparison of our transcriptomic and proteomic data with previous proteomic studies of the Flemmingsome revealed both similarities and many differences mainly due to the fact that the Flemmingsome studied HeLa kyoto MKLP!-GFP cells whereas we used HeLa CCL2 cells.

[0128] One intriguing finding from our study is the role of RND 1 in MBRs, a finding that adds a new dimension to our understanding of MBR composition and potential function. RND1, a member of the Rho family of small GTPases, is known to play crucial roles in actin cytoskeleton organization, cell adhesion, and migration. Its presence in MBRs is particularly intriguing given its involvement in neuronal development, axon guidance, and cancer progression. The enrichment of RND 1 in MBRs suggests that these structures may serve as vehicles for the intercellular transfer of signaling molecules involved in cell fate determination and tissue organization. Furthermore, considering RND 1's role in tumor suppression and its frequent downregulation in various cancers, its presence in MBRs could have implications for understanding the mechanisms of cancer progression and metastasis. This finding opens up new avenues for research into the potential role of MBRs in modulating RND 1 -dependent signaling pathways in recipient cells, potentially influencing cellular behavior in both physiological and pathological contexts. Future studies should focus on elucidating the functional consequences of RND 1 mRNA transfer via MBRs and its impact on recipient cell phenotypes.

[0129] Our finding of RND 1 enrichment in MBRs gains additional significance in light of recent research revealing the essential role of RND1 in innate immunity. Studies have shown that RND1 is induced by pro-inflammatory cytokines during viral and bacterial infections and provides protection against these pathogens through two distinct mechanisms. First, RND1 enhances NF- KB activation, leading to increased production of cytokines and chemokines crucial for the immune response. Second, RND1 suppresses pathogen replication, directly contributing to host defense. The presence of RND1 in MBRs suggests that these structures may play a previously unrecognized role in intercellular communication during immune responses, potentially facilitating the transfer of immune-related signaling molecules between cells. This finding opens up new avenues for research into the potential role of MBRs in modulating innate immune responses and their possible involvement in the body's defense against infectious diseases. Furthermore, RNDl's involvement in cell signaling pathways might influence how cells respond to viral components delivered via MBRs, potentially altering the course of infection or the host cell's antiviral response.

[0130] The comparison of our MBR Ribo-seq data with the RNA-seq analysis provides a more comprehensive and nuanced understanding of gene expression and protein synthesis within midbody remnants. While RNA-seq offers a broad view of transcriptional activity, Ribo-seq specifically captures actively translated mRNAs, providing insights into the translational landscape of MBRs. This distinction is crucial, as Ribo-seq data typically correlates better with protein abundance than RNA-seq. The integration of these two approaches allows us to identify discrepancies between transcription and translation in MBRs, potentially revealing post- transcriptional regulatory mechanisms specific to these structures. Furthermore, the nucleotide- level resolution of Ribo-seq enables the detection of small open reading frames (sORFs) and upstream ORFs (uORFs) that might be missed by RNA-seq alone. This high-resolution data could uncover novel peptides or regulatory elements unique to MBRs. By comparing the Ribo-seq and RNA-seq datasets, we can also identify genes that show significant changes at the translational level but not at the transcriptional level, providing insights into the specialized functions and regulatory mechanisms operating within MBRs during intercellular communication and cellular processes.

[0131] In conclusion, our transcriptomic analysis of MBRs has expanded our understanding of these structures beyond what was previously known from proteomic studies. The identification of cell-type specific and conserved transcripts in MBRs opens new avenues for research into their potential roles in intercellular communication, cancer progression, and developmental processes. Future studies combining transcriptomic, proteomic, and functional approaches will be crucial to fully elucidate the biological significance of MBRs in various cellular contexts.

[0132] Methods Cell culture: HeLa cells (ATCC #CCL2) were cultured in DMEM high-glucose supplemented with 10% fetal bovine serum (FBS) and 1% penicillin / streptomycin (p / s). MCF7 cells (ATCC #HTB22) were cultured in EMEM supplemented with 10% FBS, 1% p / s and 10 mg / mL insulin. hTert RPE-1 cells (kind gift from Ikeda lab) were cultured in DME / F12 supplemented with 10% FBS and 1% p / s. HepG2 cells (kind gift from Thomson lab) were cultured in EMEM supplemented with 10% FBS and 1% p / s. H9 ESC (WiCell #WA09) were cultured on Matrigel coated dishes in E8 media with supplements. iPSC cells (kind gift Wolter lab) were cultured on Cultrex coated dishes in mTeSRl media with supplements. All cells were cultured at 37°C in a humidified incubator at 5% CO2. All cells were routinely screened for mycoplasma contamination by PCR.

[0133] RNA isolation from whole cells: Asynchronous cells were harvested when 80% confluent with Trypsin or Versene, cells counted, washed with 2 mL PBS and cell pellets used for RNA extraction using the Qiagen RNeasy mini kit. RNA was eluted in RNase free water.

[0134] MBR isolation: MBRs were isolated by ultracentrifugation of cleared culture media. Asynchronous cells were cultured until 80% confluent. Culture media was collected and centrifuged at l,000xg for 10 min to clear cell debris. Cleared culture media was transferred to ultracentrifuge tubes and centrifuged at 10,000*g for 30 min at 4°C. MBR pellets were washed with 2mL sterile PBS, centrifuged at 10,000*g for 30 min at 4°C and MBR pellets used for RNA extraction using the Qiagen RNeasy mini kit. RNA was eluted in RNase free water.

[0135] Immunofluorescent staining: Cells were cultured on glass coverslips coated with Poly- D-lysine for HeLa and RPE-1 cells or coated with cultrex for iPSC cells. MBRs were isolated as above then centrifuged at 1000*g for 15 min onto poly-l-lysine coated coverslips. Cells and MBR were fixed with 4% PF A and immunostained for RND1, RacGAP and alpha-tubulin. Images were taken at 63x magnification on a zeiss spinning disk confocal microscope. The images were analyzed using Imaged.

[0136] Sequencing: Cell and MBR samples were placed in RNAse free water and stored at -80°C before sending for sequencing. Samples were sequenced by Azenta using Illumina 2xl50bp with around 350 million PE reads and data returned as paired-end FASTQ files. RNA samples were quantified using a Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA), and RNA integrity was assessed using the 4200 TapeStation (Agilent Technologies, Palo Alto, CA, USA). DNA contaminants were removed by treating the samples with TURBO DNase (Thermo Fisher Scientific, Waltham, MA, USA). A rRNA depletion sequencing library was prepared using the QIAGEN FastSelect rRNA HMR Kit (Qiagen, Hilden, Germany). For RNA sequencing library preparation, the NEBNext Ultra II RNA Library Preparation Kit for Illumina was used according to the manufacturer’s instructions (NEB, Ipswich, MA, USA). Enriched RNAs were fragmented for 15 minutes at 94 °C, followed by first- and second-strand cDNA synthesis. The cDNA fragments were end-repaired and adenylated at the 3' ends, then universal adapters were ligated to the cDNA fragments. Indexing and library enrichment were carried out with limited-cycle PCR. The libraries were multiplexed and clustered across seven lanes of a flowcell. After clustering, the flowcell was loaded onto the Illumina platform following the manufacturer’s instructions. The samples were sequenced using a 2x150 paired-end (PE) configuration. Raw sequence data (.bcl files) generated by Illumina were converted into fastq files and de-multiplexed using the Illumina bcl2fastq program (version 2.20), allowing for one mismatch in index sequence identification.

[0137] Quantification of RNA seq data: We used fastp version 0.23.4 to preprocess the FASTQ files, using default settings for paired-end reads2--. This includes quality filtering, adapter trimming, and quality profiling before and after, all in one step. We use kallisto version 0.46.1 for quantification of the reads2) We first build a kallisto index using a k-mer size of 31, and otherwise use default settings. We build the index against the Ensembl human cDNA FASTA for all genes, release 109— We then quantify using default kallisto quant settings for paired-end reads.

[0138] Transcripts per million (TPM) Analysis: To aggregate the abundance files output by kallisto into a single TPM sheet, we use tximport version 1.32.0™. Here we provided tximport the abundance files from kallisto along with a map of Ensembl transcripts to gene symbols collected from Biomart release 1092. The output of this is a TPM table aggregated to the gene symbol level.

[0139] Quality Control: To ensure reasonable counts for downstream differential expression analysis, we include only samples that contain a minimum of 5M mapped reads. All samples failing this criteria are redone. We also performed principal component analysis22and nonnegative matrix factorization2on the TPM of all samples. We then plot the samples on their first two components and verify that whole cell and MBR samples of the different types roughly colocate with one another. This is simply a rough sanity check of the data with no rigorous definition of colocation. Samples that don’t appear to colocate are dropped from downstream analysis. Differential Expression (DE): We used DESeq2 version 1.44.0 to perform differential expression analysis between MBR samples and whole cell samples of each cell type22. As with TPMs, we use tximport to first aggregate the abundance data from kallisto to the gene symbol level before running DESeq232. This is convenient, as tximport includes functionality to quickly create a DESeq2 dataset and run the analysis without the need for manual conversion. Before analysis, we drop genes that have fewer than 10 counts in at least four samples. We define differentially expressed genes as those having an adjusted p-value of less than or equal to 0.01, and having a fold change greater than or equal to 2 or less than or equal to 0.5 (2x fold change up or down).

[0140] Gene Filtering for Downstream Analysis: For downstream analysis, we filter gene symbols to a set of 19,240 protein coding genes, excluding mitochondrial and ribosomal proteins, which are abundant and can overwhelm the signal in lower expressed genes.

[0141] MBR Gene Sets for Cell Types: We performed differential expression analysis and identified sets of genes that are differentially expressed (up and down) within the MBR samples vs whole cell samples for each of our six cell types of interest. That is, we performed differential expression analysis on the MBR samples for each cell type, using the corresponding whole cell samples as a baseline. For each cell type, this resulted in up-expressed gene set sizes in roughly the low thousands for each cell type. We also then find common up-expressed genes within pairs of relevant cell types. Specifically, we performed a set intersection of up-expressed genes in HeLa and MCF7 to get a set of 186 common up-expressed genes between the two, referring to these as our cancer gene set. We did the same with the H9 and iPS to get a common set of 245 up-expressed stem genes. We also did this for RPE and HepG2, creating a common set of 153 up-expressed genes for differentiated cell types. Finally, we performed a set intersection across the up-expressed gene sets for all cell types to get a common set of 3 up-expressed genes in the MBR vs whole cell.

[0142] Library preparation for ribosome profiling: For the cell lysate and MBR samples, Thor-Ribo-Seq library preparation was performed as described previously33.

[0143] Lysate preparation: HeLa cells were cultured in a 10-cm dish. The cells were lysed in 600 pl of lysis buffer (20 mM Tris-HCl pH 7.5, 150 mM NaCl, 5 mM MgC12, 1% Triton X-100, 1 mM DTT, 100 pg / ml cycloheximide (CHX), and 100 pg / ml chloramphenicol) and treated with 25 U / ml TURBO DNase (Thermo Fisher Scientific) and incubated for 10 min on ice. After DNase treatment, the cell lysate was clarified by centrifugation at 20,000 * g for 10 min at 4 °C. The lysates were flash-frozen with liquid nitrogen and stored at -80 °C. HeLa MBR were harvested from the cell culture supernatant from 80% confluent cells, CHX and chloramphenicol was added to the medium which was centrifuged at 1000 * g for 10 min at RT to remove cell debris. The cleared supernatant was transferred to a new tube and then centrifuged at 10,000 x g for 30 min at 4 °C. The MBR pellet was washed with ice cold PBS containing CHX and chloramphenicol, centrifuged 10,000 x for 10 min at 4 °C and pellet lysed in 600 pl of lysis buffer, treated with 25 U / mL TURBO DNase and incubated for 10 min on ice. After DNase treatment, the MBR lysate was clarified by centrifugation at 20,000 x for 10 min at 4 °C. The lysates were flash-frozen with liquid nitrogen and stored at -80 °C.

[0144] Proteome sample preparation: HeLa cells were cultured in six T175 flasks to approximately 80% confluency. Midbody remnants (MBRs) were isolated from the spent culture medium, and the corresponding HeLa cell populations producing those MBRs were scraped and pelleted. Both the isolated MBRs and the cell pellets (n = 6) were lysed in RIPA buffer supplemented with protease inhibitors. Lysates were sonicated to shear DNA, and proteins were precipitated from the RIPA buffer using methanol. The methanol-RIPA mixture was centrifuged at 3,000 x for 10 min, and the resulting protein pellets were resuspended in 8 M urea with 50 mM ammonium bicarbonate (ABC). Samples were then diluted to 2 M urea in 50 mM ABC prior to reduction and alkylation with tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAM), respectively. Proteins were digested overnight at 37 °C with trypsin at a 1 :100 (w / w) enzyme-to- protein ratio. For cell samples, 30 pg of digested protein was used for desalting, and for MBR samples, 25 pg was used. Digests were acidified and desalted using C18 spin columns (Pierce).

[0145] Library preparation for RNA-Seq: For the both cell and MBR samples, total RNA was extracted from 150 pl of lysate with TRIzol LS (Thermo Fisher Scientific, 10296-010) and a Direct-zol RNA MicroPrep Kit (Zymo Research, R2062). The libraries were prepared using the SEQuoia Express Stranded RNA Library Prep Kit (Bio-Rad, 12017265).

[0146] Mapping of deep sequencing data: Sequencing data were processed as previously described34.We used fastp (ver. 0.21.0)27 for read quality filtering and adapter trimming, cutadapt (ver. 3.7)35for demultiplexing by linker barcodes, and UMI-tools (ver. 1.1.2)36 for deduplication on the basis of unique molecular identifiers.Reads that mapped to noncoding RNAs were removed. The remaining reads were then aligned to the human genome (hg38) and assigned to the GENCODE human release 44 reference using STAR(ver. 2.7.0a)37. Proteome sample preparation: HeLa cells were cultured in six T175 flasks to approximately 80% confluency. Midbody remnants (MBRs) were isolated from the spent culture medium, and the corresponding HeLa cell populations producing those MBRs were scraped and pelleted. Both the isolated MBRs and the cell pellets (n = 6) were lysed in RIPA buffer supplemented with protease inhibitors. Lysates were sonicated to shear DNA, and proteins were precipitated from the RIPA buffer using methanol. The methanol-RIPA mixture was centrifuged at 3,000 x for 10 min, and the resulting protein pellets were resuspended in 8 M urea with 50 mM ammonium bicarbonate (ABC). Samples were then diluted to 2 M urea in 50 mM ABC prior to reduction and alkylation with tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAM), respectively. Proteins were digested overnight at 37 °C with trypsin at a 1 :100 (w / w) enzyme-to- protein ratio. For cell samples, 30 pg of digested protein was used for desalting, and for MBR samples, 25 pg was used. Digests were acidified and desalted using C18 spin columns (Pierce). Dried down peptides were resolubilized into lOpL LC / MS grade 0.1% formic acid and 1 pL of this was injected for spectral analysis.

[0147] LC / MS: The liquid chromatography (LC) system used was a Dionex UltiMate 3000 and the LC conditions were as follows: buffer A is 0.1% formic acid, buffer B is 80 / 20 / 0.1% acetonitrile / water / formic acid, flow rate was 300nL / min, and the column used was a 50cm Thermo Scientific PepMap RSLC C18 column with 2pm bead size, 100A pore size, and 75pm inner diameter. After column loading in 2% B, peptide samples were eluted with a 75-minute linear gradient from 0-25%B followed by a 5 min linear gradient to 95%B, flushing with 05% B for 5 min, then requilibration to 2% B for 12 minutes. Data was acquired with a Thermo Scientific Orbitrap Fusion Lumos instrument with a standard data dependent mode. MSI acquisition conditions were as follows: cycle time of Is between MSI scans, acquisition in the Orbitrap mass analyzer with a resolution of 120K, scan range of 350-1600m / z, normalized AGC target of 250%, profile data and positive mode, and a max inject time of 50ms. Fragment ion selection was filtered using MIPS mode set to peptide and charge state set to 2-4. Dynamic exclusion was used with an n of 1 for 10 seconds, and a mass tolerance + / - lOppm. MS2 spectra were acquired in the ion trap with quadrupole isolation of 0.7, HCD activation / fragmentation with 30% collision energy, turbo scan rate, max inject time of 25ms, normalized AGC target of 300%, scan range set to auto, and positive mode with centroid data. Proteomic Informatics / Database searching and quantification: Raw data was searched with the software Proteome Discoverer v2.4. The H. Sapiens Uniprot proteome (20,353 sequences) was searched using the Sequest algorithm alongside a database of common contaminants with the following parameters: a precursor mass tolerance of lOppm, fragment ion mass tolerance of 0.6Da, max missed cleavages of 2, carbamidomethylation (+57.02) set as a fixed modification on cysteines. The following modifications were set as dynamic: oxidation (+15.99) on methionines, deamidation (+0.98) on asparagines and glutamines, and phosphorylation on serines, threonines, and tyrosines (+79.97). A concatenated database search strategy was used in the Percolator node, and an FDR of 0.05 was set. For scoring and further filtering, all default parameters were used. For label-free quantification in Proteome Discoverer, peptide precursor abundances based on intensity were used, and protein level abundance calculations were performed using summed abundances. Samples were normalized to total peptide amount per sample.

[0148] Statistics and reproducibility: All statistical analyzes were conducted using the R software version 4.1. 1 operated within the RStudio interface version 2021.09.0 + 351. For analysis of deep sequencing data, the significance of fold changes was calculated by the likelihood ratio test in a generalized linear model using the DESeq2 packagel02. We used the modified Fisher’s exact test for the calculation of p values in GO analysis and the two-sided Kolmogorov- Smirnov test for Fig. 4d, e.

[0149] STRING Analysis: Protein lists were analyzed using the STRING database (version 12) to identify protein-protein interaction networks. The analysis was performed using the full STRING network, with edges representing confidence scores. Active interaction sources included text mining, experimental data, curated databases, co-expression, neighborhood, gene fusion, and co-occurrence. The minimum required interaction score was set to 0.4 (medium confidence). Representative networks are shown in Figure 9A,B.

[0150] References

[0151] 1. Patel, S.A., Park, S., Zhu, D., Torr, E.E., Dureke, A.-G., McIntyre, A., Muzyka, N., Severson, J., and Skop, A.R. (2024). Extracellular vesicles, including large translating vesicles called midbody remnants, are released during the cell cycle. Mol. Biol. Cell 35, ar 155. doi.org / 70.1091 / mbc.e23-10-0384.

[0152] 2. Kuriyama, R., Mullins, J.M., and Skop, A.R. (2025). The Midbody and Midbody Remnant: from cellular debris to signaling organelle with diagnostic and therapeutic potential. Mol. Biol. Cell 36, re4. doi.org / 10.1091 / mbc.e25-03-0 / 20.

[0153] 3. Peterman, E., and Prekeris, R. (2019). The postmitotic midbody: Regulating polarity, sternness, and proliferation. J Cell Biology 218, 3903-397 / . doi.org / 10.1083 / jcb.201906 / 48.

[0154] 4. Jeffery, J., Sinha, D., Srihari, S., Kalimutho, M., and Khanna, K.K. (2015). Beyond cytokinesis: the emerging roles of CEP55 in tumorigenesis. Oncogene 35, 683-690. doi.org / 10.1038 / onc.2015.128.

[0155] 5. Lujan, P., Rubio, T., Varsano, G., and Kohn, M. (20 / 7). Keep it on the edge: The post-mitotic midbody as a polarity signal unit. Commun Integr Biology 10, e / 338990. doi.org / 10.1080 / 19420889.2017.1338990.

[0156] 6. Dionne, L.K., Wang, X.-J., and Prekeris, R. (2015). Midbody: from cellular junk to regulator of cell polarity and cell fate. Curr Opin Cell Biol 35, 51-55. doi.org / 10.1016 / j.ceb.2015.04.010.

[0157] 7. Antanaviciute, I., Gibieza, P., Prekeris, R., and Skeberdis, V. (2018). Midbody: From the Regulator of Cytokinesis to Postmitotic Signaling Organelle. Medicina 54, 53. doi . org / 10.3390 / medicina54040053.

[0158] 8. Farmer, T., and Prekeris, R. (2022). New signaling kid on the block: the role of the postmitotic midbody in polarity, sternness, and proliferation. Mol. Biol. Cell 33, 1-3. doi.org / 10.1091 / mbc.e21-06-0288.

[0159] 9. Crowell, E.F., Gaffuri, A.-L., Gayraud-Morel, B., Tajbakhsh, S., and Echard, A. (2014). Engulfment of the midbody remnant after cytokinesis in mammalian cells. J. Cell Sci. 127, 3840- 3851.doi.org / 10.1242 / jcs.154732

[0160] 10. Peterman, E., Gibieza, P., Schafer, J., Skeberdis, V.A., Kaupinis, A., Valius, M., Heiligenstein, X., Hurbain, I., Raposo, G., and Prekeris, R. (2017). The post-abscission midbody is an intracellular signaling organelle that regulates cell proliferation. Nat. Commun. 10, 3181. doi.org / 10.1038 / s41467-019-10871-0.

[0161] 11. Addi, C., Presle, A., Fremont, S., Cuvelier, F., Rocancourt, M„ Milin, F., Schmutz, S., Chamot-

[0162] Rooke, J., Douche, T., Duchateau, M., et al. (2020). The Flemmingsome reveals an ESCRT-to- membrane coupling via ALIX / syntenin / syndecan-4 required for completion of cytokinesis. Nat Commun 11, 1941. doi.org / 10.1038 / s41467-020-15205-z.

[0163] 12. Park, S., Dahn, R., Kurt, E., Presle, A., VanDenHeuvel, K., Moravec, C., Jambhekar, A., Olukoga, O., Shepherd, J., Echard, A., et al. (2023). The mammalian midbody and midbody remnant are assembly sites for RNA and localized translation. Dev. Cell 58, 1917-1932. e6. doi . org / 10.1016 / j . devcel .2023.07.009.

[0164] 13. McNeely, K.C., and Dwyer, N.D. (2020). Cytokinesis and postabscission midbody remnants are regulated during mammalian brain development. P Natl Acad Sci Usa 117, 9584-9593. doi.org / 10.1073 / pnas.1919658117.

[0165] 14. McNeely, K.C., and Dwyer, N D. (2021). Cytokinetic Abscission Regulation in Neural Stem Cells and Tissue Development. Curr Stem Cell Reports, 1-13. doi.org / 10.1007 / s40778-021- 00193-7.

[0166] 15. Rai, A., Greening, D.W., Xu, R., Chen, M., Suwakulsiri, W., and Simpson, R.J. (2019). Secreted midbody remnants are a class of extracellular vesicles molecularly distinct from exosomes and microparticles. Commun. Biol. 4, 400. / doi.org / 10.1038 / s42003-021-01882-z.

[0167] 16. Park, S., Patel, S.A., Torr, E.E., Dureke, A.-G.N., McIntyre, A.M., and Skop, A.R. (2023). A protocol for isolating and imaging large extracellular vesicles or midbody remnants from mammalian cell culture. STAR Protoc. 4, 102562. doi. org / 10.1016 / j.xpro.2023.102562.

[0168] 17. Suwakulsiri, W., Xu, R., Rai, A., Shafiq, A., Chen, M., Greening, D.W., and Simpson, R.J. (2023). Comparative proteomic analysis of three major extracellular vesicle classes secreted from human primary and metastatic colorectal cancer cells: Exosomes, microparticles, and shed midbody remnants. PROTEOMICS 11, e2300057. doi.org / 10.1002 / pmic.202300057.

[0169] 18. Chaigne, A., Labouesse, C., White, I. J., Agnew, M., Hannezo, E., Chalut, K.J., and Paluch, E.K. (2020). Abscission Couples Cell Division to Embryonic Stem Cell Fate. Dev Cell, doi. org / 10.1016 / j. devcel.2020.09.001.

[0170] 19. Ettinger, A.W., Wilsch-Brauninger, M., Marzesco, A.-M., Bickle, M., Lohmann, A., Maliga,

[0171] Z., Karbanova, J., Corbeil, D., Hyman, A.A., and Huttner, W.B. (2011). Proliferating versus differentiating stem and cancer cells exhibit distinct midbody-release behaviour. Nat Commun 2, 503. doi.org / 10.1038 / ncommsl511.

[0172] 20. Chen, C.-T., Ettinger, A.W., Huttner, W.B., and Doxsey, S.J. (2012). Resurrecting remnants: the lives of post-mitotic midbodies. Trends Cell Biol 23, 118-128. doi. org / 10.1016 / j.tcb.2012.10.012.

[0173] 21. Kuo, T.-C., Chen, C.-T., Baron, D., Onder, T.T., Loewer, S., Almeida, S., Weismann, C.M.,

[0174] Xu, P., Houghton, J.-M., Gao, F.-B., et al. (2011). Midbody accumulation through evasion of autophagy contributes to cellular reprogramming and tumorigenicity. Nat Cell Biol 13, 1214— 1223. doi.org / 10.1038 / ncb2332. 22. Goering, R., Arora, A., Pockalny, M.C., and Taliaferro, J.M. (2023). RNA localization mechanisms transcend cell morphology. eLife 12, e80040. doi.org / 10.7554 / elife.80040.

[0175] 23. Fishman, L., Modak, A., Nechooshtan, G., Razin, T., Erhard, F., Regev, A., Farrell, J. A., and Rabani, M. (2024). Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq. Nat. Commun. 15, 3104. doi . org / 10.1038 / s41467-024-47290-9.

[0176] 24. Chaigne, A., and Brunet, T. (2022). Incomplete abscission and cytoplasmic bridges in the evolution of eukaryotic multicellularity. Curr Biol 32, R385-R397. doi. org / 10.1016 / j. cub.2022.03.021.

[0177] 25. Lever, J., Krzywinski, M., and Altman, N. (2017). Principal component analysis. Nat. Methods

[0178] 14, 641-642. doi.org / 10.1038 / nmeth.4346.

[0179] 26. Ashbumer, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P.,

[0180] Dolinski, K., Dwight, S.S., Eppig, J.T., et al. (2000). Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25-29. doi. org / 10. 1038 / 75556.

[0181] 27. Chen, S., Zhou, Y., Chen, Y., and Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884— i890. doi. org / 10.1093 / bioinformatics / bty560.

[0182] 28. Bray, N.L., Pimentel, H., Melsted, P., and Pachter, L. (2016). Near-optimal probabilistic RNA- seq quantification. Nat. Biotechnol. 34, 525-527. doi. org / 10.1038 / nbt.3519.

[0183] 29. Cunningham, F., Allen, J.E., Allen, J., Alvarez -Jarreta, J., Amode, M R., Armean, I.M.,

[0184] Austine-Orimoloye, O., Azov, A.G., Barnes, I., Bennett, R., et al. (2021). Ensembl 2022. Nucleic Acids Res. 50, D988-D995. doi.org / 10.1093 / nar / gkab 1049.

[0185] 30. Soneson, C., Love, M.I., and Robinson, M.D. (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. FlOOOResearch 4, 1521. doi. org / 10.12688 / flOOOresearch.7563.1.

[0186] 31. Zhu, R., Liu, J.-X., Zhang, Y.-K., and Guo, Y. (2017). A Robust Manifold Graph Regularized

[0187] Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering. Mol. : A J. Synth. Chem. Nat. Prod. Chem. 22, 2131. doi.org / 10.3390 / molecules22122131.

[0188] 32. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi. org / 10.1186 / sl3059-014- 0550-8.

[0189] 33. Mito, M., Shichino, Y., and Iwasaki, S. (2023). Thor-Ribo-Seq: ribosome profiling tailored for low input with RNA-dependent RNA amplification. bioRxiv, 2023.01.15.524129. doi.org / 10.1101 / 2023.01.15.524129.

[0190] 34. Tomuro, K., and Iwasaki, S. (2025). Advances in ribosome profiling technologies. Biochem. Soc. Trans. doi.org / 10.1042 / bst20253061. 35. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetJ. 17, 10-12. doi.org / 10.14806 / ej.17.1.200.

[0191] 36. Smith, T., Heger, A., and Sudbery, I. (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491-499. doi.org / 10.1101 / gr.209601.116. 37. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson,

[0192] M., and Gingeras, T.R. (2012). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21. doi . org / 10.1093 / bioinformati cs / bts635.

Claims

CLAIMSWhat is claimed:

1. A method compri sing :(a) obtaining a biological sample from a subject, wherein the sample comprises a midbody remnant (MBR);(b) isolating the MBR from the sample, wherein the MBR comprises RNA;(c) analyzing the RNA in the MBR; and(d) using the RNA analysis to identify a cell type from which the MBR originated.

2. The method of claim 1, wherein the RNA comprises transcripts from one or more of the following genes: PNRC1, IRF1, NFKBIZ, KLF4, KLF6, FOSB, FOS, JUN, NUPR1, NFKBIA, TEX14, PLK2, KIF23, ZFP36, IFRD1, ARL4A, SAT1, BTG2, BIRC3, CXCL2, AREG, ARC, Hl -4 and combinations thereof.

3. The method of claim 1 or 2, wherein the RNA comprises transcripts from one or more of the following genes: RND1, RABB, NET1, SH3GL2, SNAI1, P2RY10, LRRK1, DNAAF3, ARC, KLF4, FOS, RGS2, CD3E, DLL4, FRMD1, FRZB, GRIA2, PTAFR, PTPRN, SH3RF2, SYT5, PRX, TRAK2, SCGB3A2, FAM166A, MAFB, SCGB32A, TP53INP2, INPP5D, HHLA1, SLC17A7, LYPD3, EAF2, ACHE, CTNND2, ARPP21, KCNA2, FAM135B, MR0H7, TULP2, IRX1, FCN1, IRF8, LRRC25, IGF2, RASAL3, RGSL1, and combinations thereof.

4. The method of any one of the preceding claims, wherein the transcripts from genes comprise RND1, RABB, and PRX.

5. The method of any one of claims 1-4, wherein the cell type of origin comprises stem cells, cervical cells, breast cells, liver cells, retinal cells, lung cells, gastrointestinal cells, cardiac cells, central nervous system cells, bone cells, prostate cells, kidney cells, blood cells, epidermal cells, bladder cells, or pancreatic cells.

6. The method of claim 5, further comprising comparing the RNA transcript levels in the MBR to RNA transcript levels in MBRs from a control sample, wherein the cell type is identifiedas a cancer cell when the quantity per MBR of transcripts from one or more of the genes is increased in the biological sample from the subject relative to the control sample.

7. The method of any one of claims 1-6, wherein the subject has or is suspected of having a proliferative disease.

8. The method of claim 7, wherein the proliferative disease comprises cancer.

9. The method of any one of claims 1-8, wherein the RNA comprises transcripts from one or more of the following genes: MMRN1, HBB, LRCH2, NEFM, CACNG7, PCDH18, COL11A1, GRIN2B, RGS18, IRAG2, PCSK5, ARC, DPPA4, VCAN, FAT3, FAM166A, LIN28A, NTRK3, GABRB3, CAMK2A, RGSL1, SCUBE1, BCL11A, ANKDD1A, RABB, DCHS1, PTPRZ1, NRXN2, RND1, DYSF, SPRY4, HHLA1, JAKMIP2, FCN1, ATP1B2, SEMA5B, FAM135B, HMGA2, ADAMTS4, ALG1L2, TRIM71, NCALD, SHANK 1, KLHL3, TRAK2, RGS2, UNC13A, DENND2A, P2RY10, TP53INP2 and combinations thereof.

10. The method of claim 9, further comprising comparing the RNA transcript levels in the MBR to RNA transcript levels in MBRs from a control sample, wherein the cell type is identified as a cancer cell when the quantity per MBR of transcripts from one or more of the genes is increased in the biological sample from the subject relative to the control sample.

11. The method of any one of claims 1-10, wherein the level of differentiation of the cell type is identified.

12. The method of claim 11, wherein the cell type is identified as lacking differentiation when the RNA comprises transcripts from one or more of the following genes: FOLR3, RAX, OSR1, SIX3, DLK1, GATA3, ANKRD1, IGF2, DLX2, ATF7-NPFF, GOLGA6A, PHLDA2, ZNF703, SH3RF2, ALG1L2, KLF5, TMEM212, MPL, GDF15, PTGER4, TMEM249, LEFTY2, CDKN1A, DUSP10, COL3A1, COL1A1, ARHGAP19-SLIT1, PLK2, CHRNB4, FLNC, CDH6, IRF1, RASD1, NDRG1, CNTN3, LEFTY1, ID4, IRX1, PLAAT5, GALNT5, BHLHE40,ADRB2, AP0LD1, SMCO3, UTF1, ETFRF1, GADD45B, TNFSF9, SLC17A7, BMP4 and combinations thereof.

13. The method of claim 11, wherein the cell type is identified as differentiated when the RNA comprises transcripts from one or more of the following genes: MUC16, HBB, DNAH8, TBX5, TNNT3, CD3E, MYH6, COLGA6L24, BPIFB3, OTOG, MUC5AC, MZB1, MAFB, MUC3A, OTOF, NRSN1, PGLYRP3, USH2A, EYA2, RABB, DU0X2, SLC4A1, FBN3, RND1, COL23A1, LCN15, HTR5A, GPR174, GRM4, PPFIA2, KCNN1, CD84, NRXN2, MUC4, HMCN2, MY016, DCHS1, DSCAM, FAM166A, COL20A1, KLF4, ABCA4, HBA1, FYB1, CFAP57, ELAVL3, ABCA6, WSCD2, RUNX1T1, RYRland combinations thereof.

14. The method of any one of the previous claims, wherein the RNA comprises transcripts from one of more of the following genes:(a) ARX, CFC1, THSD7B, POTEG, 0R2A1, VWC2L, TP53TG3E, HBB, ASB15, BP1FB6, HCN1, GRIN2B, ARC, CACNA1I, ADAM28, FOSB, RASD1, DISP3, EGR3, GALR1, and the cancer is a gynecological cancer or cervical cancer; or(b) P3R3URF-PIK3R3, COL11A1, MMRN1, CACNG7, GRIN2B, ARPP21, D0K5, MDFI, NEFM, MUC17, PCSK5, RABB, FLNC, TRAK2, SFRP2, ARRDC4, PRKCB, NET1, VCAN, HMGA2, and the cancer is a breast cancer; or(c) CRYBA2, CRYBB1, HBB, BORCS7-ASMT, ARL2-SNX15, GHSR, MS4A14M MKX, FOXL3, CT47B1, MUC16, STUM, NRSN1, PTPRN2, SHISAL2A, IGF2, C9orfl52, MUC5AC, MUC3A, ANXA2R, and the cancer is an epithelial or eye cancer; or(d) MUC16, DNAH8, G0LGA6L1, GOLGA624, H3C11, FNDC1, SPTA1, SCL6A5, H0XB3, MYH2, PCSK5, MZB1, ESRI, HBB, ARC, TMEM255B, EYA2, ABCA12, ANKFN1, MUC5AC, and the cancer is a liver cancer; or(e) TFF1, SIX3, DLX5, NR2F2, DDX3Y, EN2, 0R13H1, BB0X1, TMPRSS11D, ECM2, LRRK1, SCGB3A2, AHNAK, GABRP, EGR3, TSHZ2, FOSL1, EGR1, PAPP A, BTG2 and the cell is an undifferentiated cell; or(f) MT3, PITX2, NEUR0G1, ITPKA, CH25H, GRP, CXCL11, RAX, ANTXRL, CHCT1, IL10RA, DLX2, CNTN3, PTGER4, DIO3, IRF4, C2CD4B, DLL4, LEFTY1, DLX3, and the cell is an undifferentiated cell.

15. The method of any one of the preceding claims, wherein analyzing the RNA comprises sequencing, polymerase chain reaction, or real-time polymerase chain reaction to determine RNA transcript identity and / or RNA transcript levels.

16. The method of any one of the preceding claims, wherein the MBR is isolated by a method comprising combining a polyethylene glycol (PEG) solution with the biological sample, incubating the PEG solution and the biological sample for at least 4 hours and recovering the MBR, wherein the PEG solution comprises between 0.5 and 5 % PEG.

17. The method of claim 16, further comprising labeling the recovered MBR with an affinity reagent specific for a protein selected from the group consisting of MKLP1, CD9, MgcRACGAPl, PLK1, AURK, CITK, ANNEXIN 11, TEX 14 and ARC.

18. A method for detecting a proliferative disease is a subject, the method comprising:(a) obtaining a biological sample from the subject, wherein the sample comprises a midbody remnant (MBR);(b) isolating the MBR from the sample, wherein the MBR comprises RNA;(c) analyzing the RNA in the MBR; and(d) using the RNA analysis to identify a cell type from which the MBR originated.

19. The method of claim 18, further comprising labeling the recovered MBR with an affinity reagent specific for a protein selected from the group consisting of MKLP1, CD63, CD9, MgcRACGAPl, PLK1, AURK, CITK, ANNEXIN 11, TEX 14 and ARC.

20. The method of claim 18 or 19, wherein analyzing the RNA comprises determining an amount or level of RNA in the biological sample.

21. The method of claim 20, further comprising comparing the amount or level of RNA in the MBR to a level or quantity of the RNA in MBR from a control sample, wherein an increase in thelevel of RNA in the biological sample compared to that of the control sample is indicative of the proliferative disease in the subject.

22. The method of any one of claims 18-21, wherein the RNA comprises transcripts from one or more of the following genes: PNRC1, IRF1, NFKBIZ, KLF4, KLF6, FOSB, FOS, JUN, NUPR1, NFKBIA, TEX14, PLK2, KIF23, ZFP36, IFRD1, ARINA, SAT1, BTG2, BIRC3, CXCL2, AREG, ARC, Hl -4 and combinations thereof.

23. The method of any one of claims 18-22, wherein the RNA comprises transcripts from one or more of the following genes: RND1, RAB13, NET1, SH3GL2, SNAI1, P2RY10, LRRK1, DNAAF3, ARC, KLF4, FOS, RGS2, CD3E, DLL4, FRMD1, FRZB, GRIA2, PTAFR, PTPRN, SH3RF2, SYT5, PRX, TRAK2, SCGB3A2, FAM166A, MAFB, SCGB32A, TP53INP2, INPP5D, HHLA1, SLC17A7, LYPD3, EAF2, ACHE, CTNND2, ARPP21, KCNA2, FAM135B, MR0H7, TULP2, IRX1, FCN1, IRF8, LRRC25, IGF2, RASAL3, RGSL1 and combinations thereof.

24. The method of any one of claims 18-23, wherein the RNA comprises transcripts from one or more of the following genes: MMRN1, HBB, LRCH2, NEFM, CACNG7, PCDH18, COL11 Al, GRIN2B, RGS18, IRAG2, PCSK5, ARC, DPPA4, VCAN, FAT3, FAM166A, LIN28A, NTRK3, GABRB3, CAMK2A, RGSL1, SCUBE1, BCL11A, ANKDD1A, RAB I 3, DCHS1, PTPRZ1, NRXN2, RND1, DYSF, SPRY4, HHLA1, JAKMIP2, FCN1, ATP1B2, SEMA5B, FAM135B, HMGA2, ADAMTS4, ALG1L2, TRIM71, NCALD, SHANK1, KLHL3, TRAK2, RGS2, UNC13A, DENND2A, P2RY10, TP53INP2 and combinations thereof.

25. The method of any one of claims 18-24, wherein the RNA comprises transcripts from one of more of the following genes:(a) ARX, CFC1, THSD7B, POTEG, 0R2A1, VWC2L, TP53TG3E, HBB, ASB15, BP1FB6, HCN1, GRIN2B, ARC, CACNA1I, ADAM28, FOSB, RASD1, DISP3, EGR3, GALR1, and the cancer is a gynecological cancer or cervical cancer; or(b) P3R3URF-PIK3R3, COL11A1, MMRN1, CACNG7, GRIN2B, ARPP21, D0K5, MDFI, NEFM, MUC17, PCSK5, RABB, FLNC, TRAK2, SFRP2, ARRDC4, PRKCB, NET1, VCAN, HMGA2, and the cancer is a breast cancer; or(c) CRYBA2, CRYBB1, HBB, B0RCS7-ASMT, ARL2-SNX15, GHSR, MS4A14M MKX, F0XL3, CT47B1, MUC16, STUM, NRSN1, PTPRN2, SHISAL2A, IGF2, C9orfl52, MUC5AC, MUC3A, ANXA2R, and the cancer is an epithelial or eye cancer; or(d) MUC16, DNAH8, G0LGA6L1, GOLGA624, H3C11, FNDC1, SPTA1, SCL6A5, H0XB3, MYH2, PCSK5, MZB1, ESRI, HBB, ARC, TMEM255B, EYA2, ABCA12, ANKFN1,MUC5AC, and the cancer is a liver cancer.

26. The method of any one of claims 18-25, wherein analyzing the RNA comprises sequencing, reverse transcription-polymerase chain reaction, or reverse transcription-real-time polymerase chain reaction to determine RNA transcript identity and / or RNA transcript levels27. The method of any one of the preceding claims, wherein the biological sample comprises plasma, serum, cerebral spinal fluid, urine, blood, saliva or tissue.

28. The method of any one of the preceding claims, wherein the MBR are isolated using at least one protein selected from the group consisting of MKLP1, CD63, CD9, MgcRACGAPl, PLK1, AURK, CITK, ANNEXIN 11, TEX14 and ARC affinity reagent.

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