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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Nanotechnol. Author manuscript; available in PMC 2014 February 1.
Published in final edited form as:
Nat Nanotechnol. 2013 August; 8(8): 580–586.
Published online 2013 July 28. doi:  10.1038/nnano.2013.142
PMCID: PMC3776593

Autonomous Molecular Cascades for Evaluation of Cell Surfaces


Molecular automata are mixtures of molecules that undergo precisely defined structural changes in response to sequential interactions with inputs14. Previously studied nucleic acid-based-automata include game-playing molecular devices (MAYA automata3,5) and finite-state automata for analysis of nucleic acids6 with the latter inspiring circuits for the analysis of RNA species inside cells7,8. Here, we describe automata based on strand-displacement9,10 cascades directed by antibodies that can analyze cells by using their surface markers as inputs. The final output of a molecular automaton that successfully completes its analysis is the presence of a unique molecular tag on the cell surface of a specific subpopulation of lymphocytes within human blood cells.

The problem of labeling a narrow subpopulation within a much larger population of related cells occurs often because of the need to specifically tag a particular cell type for the purpose of elimination11, analysis and isolation12, or imaging13. The problem could be readily addressed in a direct manner if targeted subpopulations could have some unique cell-surface marker13 against which antibodies can be raised. However, as best illustrated through an example of a cancer therapy utilizing antibody-drug conjugates (ADCs), markers are most often shared by non-targeted cells, leading, in this case, to off-target toxicities13. In order to uniquely target cells that do not have any distinctive marker on their surfaces, we need to use a set of multiple markers for each subpopulation in a Boolean manner. Molecular automata with structural changes (“state transitions”) coupled to the sequential recognition of a selected set of cell surface markers might be able to contract the set into a single tag and thus provide a unique handle for the targeted cells. Or, in the language of molecular computing14,15, these molecular devices would autonomously, i.e., without any human participation, evaluate Boolean functions on cell surfaces with surface markers as inputs and a tag as an output.

We chose to utilize blood cells as targets for molecular automata, because these are the most exhaustively studied examples of cells16 with lineages and stages of differentiation defined by the presence or absence of multiple cell-surface markers. They are commonly characterized by flow cytometry via different levels of expression of multiple cell surface markers14 known as Clusters of Differentiation or CDs, with CD45, CD20, CD3, and CD8 used as examples in this work. We show in Fig. 1 the basic design principles for automata that will tag lymphocytes with targeted CD markers characteristic for B-cells, i.e., CD45+CD20+ cells, in the presence of non-targeted CD45+CD20 cells (e.g., CD45+CD3+, T-cells).

Figure 1
Design considerations for automata operating on cell surfaces

The exact “program” (i.e., conditional sequential transitions) that the automata will execute on the surfaces of lymphocytes, will be defined by sets of antibodies against CD markers Mi which direct the cascade (see Fig. 1 with CD45 and CD20 as Mi’s). We started with the well-established off-the-shelf antibodies targeting CD markers: αCD45; αCD45RA; αCD20 (Rituximab); αCD3; and αCD8. All of these antigens are present at 80,000–200,000 copies per cell surface on targeted subpopulations of lymphocytes, ensuring a strong signal when measured by flow cytometry. These antibodies were conjugated17 with a set of partially complementary oligonucleotides (1●2, 3●4, and 5●6) optimized to execute modified strand-displacement cascades9 when triggered with oligonucleotide 0 (Fig. 1b,c, Supplementary Fig. S1–S6). Once turned on, the automata based on these antibody conjugates would perform ‘if yesMi then proceed’ or‘if notMi then proceed’ assessments of Mi markers on the surface of individual cells via oligonucleotide transfers enabled by sequential exposure of new toeholds (cf. Figure 1b) and driven by the formation of more strongly complementary oligonucleotides (0●1, 2●3, and 4●5).

The first step in demonstrating automata is to test their ability to evaluate two surface markers (see Fig. 2a for yesCD45yesCD20 experiment, functionally equivalent to Boolean CD45andCD20) and to selectively label one targeted subpopulation within a population of peripheral blood mononuclear cells (PBMCs). We constructed all possible automata that could assess combinations of two out of three markers, CD45 (a marker of nucleated hematopoietic cells), CD20 (a B-cell marker), and CD3 (a pan-T-cell marker). Two of these automata are capable of successful completion of their program: yesCD45yesCD20 would operate (label) only on B-cells (Fig. 2a) and yesCD45yesCD3 would operate only on T-cells (Supplementary Fig. S7). The third possible two-step automaton, yesCD3yesCD20 is a negative control, because no subpopulation displays these two markers at the same time (Supplementary Fig. S7c). The operation of these automata is equivalent to asking: “Is this cell a nucleated hematopoietic cell?” (yesCD45) followed by, in the case of the first automaton, “Is this a nucleated hematopoietic cell from a B-cell lineage?” (yesCD20) and, in the case of the second automaton, “Is this nucleated hematopoietic cell from the T-cell lineage?” (yesCD3). In all these automata, if both questions are answered positively in a row, the reaction performed, on an example of B-cells, will be: 0 + 1●2αCD45 + 3●4αCD200●1 + αCD452●3 + αCD204, with targeted subpopulations displaying a newly uncovered single-stranded oligonucleotide, 4. This one marker then contains the same information as traditional multicolor labeling with the same antibodies that we used in construction of automata and that we would otherwise use to characterize the immunological phenotype of these cells (e.g., as CD45+CD20+). Additionally, we set up a system so that the output oligonucleotide (4) would interact with a solution-phase label as in: αCD204 + 5●6αCD204●5 + 6 (where 5 is labeled with fluorescein), thus allowing direct analysis by flow cytometry of the response of targeted cells within a heterogeneous population of cells to the cascade. In order to assess the full operation of automata, we labeled 1 with Cy5, so both its removal and subsequent acquisition of fluorescein by αCD204, on the cell surface, could be monitored simultaneously in real time.

Figure 2
Demonstration of automata assessing the presence of two cell surface markers

In our experiments, the first two automata successfully labeled only surfaces of either B-(CD45+CD20+) or T-(CD45+CD3+) cells (Fig. 2a and b and Supplementary Fig. S7, S8), with each outcome confirmed three or more times on individual human blood samples and monitored by multicolor flow cytometry. From these same components we also made an automaton that could label the surfaces of both B- and T-cells by using 3●4αCD20 and 3●4αCD3 in the same solution (cf., Supplementary Fig. S7e); a possible presentation of this automaton is the demonstration of an or function, as in yesCD45(yesCD20oryesCD3). In control experiments, we also confirmed that the automata worked on enriched cell subpopulations with correct marker combinations (B- or T-cells).

We then studied in more detail negative controls, that is, antibody directed cascades that could occur only between markers on separate cells (between two subpopulations). Using the third possible two-step automaton introduced above, yesCD3yesCD20, we observed no labeling within the time-frame of our experiment, indicating that the T-cells did not visibly exchange elements with B-cells either through diffusion or through direct physical contact of cells (Supplementary Fig. S9). We also separated T- and B-cells, labeling the former with 1●2−αCD3, the latter with 3●4−αCD20. Upon remixing the cells, we observed no crosstalk between different lineages, within the detection limits of the flow cytometer (Supplementary Fig. S9). Finally, we demonstrated that automata yesCD20yesCD45, with the inverted order of assessing the cell, worked without labeling any CD45+CD20 cells (Supplementary Fig. S10). All of these experiments demonstrate low noise in the automata in the absence of an excess of elements in the solution-phase (i.e., they demonstrate minimal tagging of cells via diffusion or by direct contact between cells). In order to estimate the effects of washing away excess of antibody conjugates, we studied automata yesCD3yesCD20 and yesCD3(yesCD20ORyesCD8) without prior removal of the excess components from the solution. In both cases we observed changes in the fluorescence of non-target cells, albeit several-fold weaker than in the case of targeted cells (Supplementary Fig. S9), indicating that proximity-based interactions on a single cell were dominant.

The structures comprising these two-step automata were adjusted to enable labeling cells with fluorescent oligonucleotides only in the absence of a CD marker, that is, an ‘if notMi then proceed’ function (notMi, Fig. 3, Supplementary Fig. S11). During the differentiation of T-cells, from naïve to memory, there is a transition in expression of two different isoforms of CD45, CD45RA and CD45RO, and we created an automaton assessing the presence of isoforms of CD45 on CD8+ T-Cells, with the CD45RA isoform inhibiting the cascade. The automaton yesCD8notCD45RA consisted of 3●4αCD8 and 5*●6*αCD45RA triggered by 2 in the presence of solution-phase 5●6 (where 5 is labeled with fluorescein). All cells that responded to the automaton and acquired 5 from solution-phase strongly expressed CD45RO, that is, they were CD45RA cells (Fig. 3b, Supplementary Fig. S11b). This was in contrast with CD8+CD45RA+ T-cells, namely CD45RO or CD45ROdim, which were hindered in acquiring 5 due to competition with 5* from CD45RA in proximity to CD8-displaying 4, instead forming 5*●4αCD8 (Fig. 3, Supplementary Fig. S11). It should be noted that the ‘if notMi then proceed’ function is limited, until a threshold9 function is introduced, by the ratio of levels of expression of individual markers on the cell surface.

Figure 3
Demonstration of automata assessing the absence of a cell surface marker

At this point, we had established three types of transitions that could be used to build larger automata, yesMi, notMi, and or (the last function consisting of adding to the cells two antibodies conjugated to identical oligonucleotide components). As an example of the feasibility of building more complex automata from these simple transitions, we proceeded to build an automaton with a three-step cascade, evaluating the presence of up to three markers, and executing on the cell surface yesCD45yesCD3yesCD8 (the third question: “Is this nucleated hematopoietic cell of T-Cell lineage a CD8 positive cell?”). In this automaton, the surface of CD8+ cells enabled the following reaction: 0 + 1●2αCD45 + 3●4αCD3 + 5●6αCD8 + 7●8 → 0●1 + αCD452●3 + αCD34●5 + αCD86●7 + 8. The labeling scheme allowed us to monitor each step in this cascade via flow cytometry in real time (Fig. 4,b, Supplementary Fig. S12). This automaton was successfully demonstrated on targeted cells, with changes in fluorescence on cells being consistent with changes in distances between various components upon each step in the cascade (the first step is monitored by the removal of Pacific Blue, second by the drop in Cy5 due to quenching, and third by the acquisition of fluorescein from solution).

Figure 4
Demonstration of automata assessing the presence of three markers (CD45, CD3 and CD8) on the surface of the cell

Finally, we decided to test our automata under conditions that could lead to applications. We were able to demonstrate: (1) that we could isolate with a purity equivalent to a standard isolation protocol, fluorescein-labeled cells after a yesCD45yesCD3 automaton; for this we used a standard method for the isolation of cells (Fig. 5a, using anti-fluorescein antibody conjugated to magnetic beads); and (2) that an automaton (we used yesCD3yesCD8) can function in whole blood such that we could simply add automata components to the mixture all together prior to triggering the reaction (Fig. 5b). The former demonstration was important, because it showed that there is no detectable decrease in purity of isolated cells between a single step automaton-based procedure (in situ cascade) and the standard separation protocol based on individual separation steps for each CD marker. The latter experiment also established that blood components did not interfere with the cascades. Together with demonstrations that interactions via solution-phase information transfer do not represent major pathways in labeling cells (vide supra and Supplementary Fig. S9) this experiment opens up the possibility of using automata for labeling and eventually eliminating cells in vivo, depending on the pharmacokinetic properties of our conjugates.

Figure 5
Demonstrations of a potential for practical applications

In conclusion, we have established that a combination of antibodies and oligonucleotide-based reaction cascades can operate as molecular automata to assess the presence or absence of cell surface markers on living human cells. From the perspective of molecular automata, these results extend their use beyond the analysis by transfecting oligonucleotides into cells7,8, thereby permitting new operations on the surfaces of native cells. Unlike previous utilizations of proximity principles such as bispecific antibodies18 or proximity ligation reactions19 our approach can be readily expanded to more complex logic operations and to the protection of cells through a not transition. The demonstrated systems contribute as well to the emerging field of molecular robotics2022. One approach to cell analysis with molecular robots is to increase the complexity of individual nanoparticles using self-assembly of DNA nanoobjects displaying multiple aptameric locks22. We offer here an alternative and potentially simpler method: we employ a number of elementary components that are brought together by the cell surface to execute more complex programmable (automata) functions, an approach that is conceptually similar to that of distributed robotics paradigms23.

Selected Methods

Detailed protocols, all sequences and their optimization, and full characterization of all synthesized reagents are provided in the Supplementary Material. Briefly: Oligonucleotides were coupled to antibodies, unless stated otherwise, in a two-step procedure: (i) DTT was used under conditions that reduce interchain disulfide bonds; (ii) 5’-end maleimide-functionalized oligonucleotides were coupled to the reduced antibody sulfhydryls, and the products were purified using gel filtration. One biotinylated antibody was used in the notCD45RA cascade, with streptavidin used to cross-link it to biotinylated oligonucleotides; this procedure was performed directly on cells without purification of conjugates (in this case negative controls with no streptavidin and no antibody were successfully run as well). Reagents were added to cell suspensions, and in all experiments involving PBMC’s reagents were removed from solution by centrifugation. In whole blood experiments (Fig. 5), reagents were left in blood to mimic the conditions for potential in vivo applications.

Supplementary Material



The research presented in this paper, as well as past attempts that eventually led to the current design, were supported by: NIH (R21CA128452, RC2CA147925, R21EB014477, RGM104960) to SR and MNS, NSF (CCF-0218262, CCF-0621600, ECCS-1026591, CBET-1033288), NASA (NAS2-02039), and a fellowship by the Lymphoma and Leukemia Foundation (CPD Award) to MNS. We thank Drs. John Loeb, Eric Meffre, and Darko Stefanovic for their advice and comments on the manuscript.


Author’s contributions: MR was the principal experimenter on cells, while ST carried out conjugations and optimized cascades in solution phase; PP performed exploratory experiments, AD and SK model experiments on beads. SR is the responsible author in charge of flow cytometry experiments, ST and MNS of non-cell-based experiments. MNS and VB designed and put early proposals for implementation of molecular computing on cell surfaces and settled on lymphocytes as targets. MR, ST, SR, and MNS analyzed the data; SR and MNS were the primary designers of the experiments and are most responsible for the structure of the presentation in this paper, while MNS wrote the initial draft of this manuscript.

Supplementary Materials

Materials and Methods

Figs. S1 to S12

Supplementary References


1. Rothemund P. A DNA and restriction enzyme implementation of Turing Machines. DNA based computers. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. 1996;(27):75–120.
2. Benenson Y, Paz-Elitzur T, Adar R, Keinan E, Livneh Z, Shapiro E. Programmable and autonomous computing machine made of biomolecules. Nature. 2001;414:430–434. [PMC free article] [PubMed]
3. Stojanovic MN, Stefanovic D. A deoxyribozyme-based molecular automaton. Nature Biotechnol. 2003;21:1069–1074. [PubMed]
4. Wang Z-G, Elbaz J, Remacle F, Levine RD, Willner I. All DNA finite-state automata with finite memory. Proc. Nat. Ac. Sci. (USA) 2010;107:21996–22001. [PubMed]
5. Pei R, Matamoros E, Liu M, Stefanovic D, Stojanovic MN. Training a molecular automaton to play a game. Nature Nanotech. 2010;5(11):773–777. [PubMed]
6. Benenson Y, Gil B, Ben-Dor U, Adar R, Shapiro E. An Autonomous Molecular Computer for Logical Control of Gene Expression. Nature. 2004;429:423–429. [PMC free article] [PubMed]
7. Rinaudo K, Bleris L, Maddamsetti R, Subramanian S, Weiss R, Benenson Y. A universal RNAi based logic evaluator that operates in mammalian cells. Nature Biotechnology. 2007;25:795–801. [PubMed]
8. Xie Z, Wroblewska L, Prochazka L, Weiss R, Benenson Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science. 2011;333:1307–1311. [PubMed]
9. Seelig G, Soloveichik D, Zhang DY, Winfree E. Enzyme-Free Nucleic Acid Logic Circuits. Science. 2006;314:1585–1588. [PubMed]
10. Qian L, Winfree E, Bruck J. Neural network computation with DNA strand displacement cascades. Nature. 2011;475:368–372. [PubMed]
11. Weiner LM, Surana R, Wang S. Monocolonal antibodies: versatile platforms for cancer immunotherapy. Nature Rev. Immunology. 2010;10:317–327. [PMC free article] [PubMed]
12. Welte Y, Adjaye J, Lehrach HR, Regenbrecht CR. A. Cancer Stem cells in solid tumors, elusive or illusive? Cell Communication and Signaling. 2010;8:6. [PMC free article] [PubMed]
13. Ichise M, Harris PE. Imaging of β-cell mass and function. J. Nucl. Med. 2010;51:1001–1004. [PMC free article] [PubMed]
14. De Silva AP, Uchiayam S. Molecular Logic and Computing. Nature Nanotechnology. 2007;2:399–410. [PubMed]
15. Katz E, Privman V. Enzyme-based logic systems for information processing. Chem. Soc. Rev. 2010;39:1835–1857. [PubMed]
16. Bendall SC, Simonds EF, Qiu P, Amir el-AD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe’er D, Tanner SD, Nolan GP. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332(6030):687–696. [PMC free article] [PubMed]
17. Liu H, Chumsae C, Gaza-Bulseco G, Hurkmans K, Radziejewski CH. Ranking the susceptibility of disulfide Bonds in Human IgG1 antibodies by reduction, differential alkylation, and LC-MS analysis. Analytical Chemistry. 2010;82:5219–5226. [PubMed]
18. Holmes D. Buy buy bispecific antibodies. Nature Rev. Drug Discovery. 2011;10:798. [PubMed]
19. Söderberg O, Gullberg M, Jarvius M, Ridderstråle K, Leuchowius K-J, Jarvius J, Wester K, Hydbring P, et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nature Methods. 2006;3(12):995–1000. [PubMed]
20. Douglas SM, Bachelet I, Church GM. A logic-gated nanorobot for targeted transport of molecular payloads. Science. 2012;335:831–834. [PubMed]
21. Gu H, Chao J, Xiao S-J, Seeman NC. A proximity-based programmable DNA nanoscale assembly line. Nature. 2010;465:202–205. [PMC free article] [PubMed]
22. Lund K, Manzo AJ, Dabby N, Michelotti N, Johnson-Buck A, Nangreave J, Taylor S, Pei R, Stojanovic MN, Walter NG, Winfree E, Yan H. Molecular robots guided by prescriptive landscapes. Nature. 2010;13:206–210. [PMC free article] [PubMed]
23. Distributed Autonomous Robotic Systems: The 10th International Symposium; 2012. (Springer Tracts in Advanced Robotics)