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Archive for the ‘Nanotechnology for Drug Delivery’ Category


Nano-guided cell networks as conveyors of molecular communication

Nature Communications
6,
Article number:
8500
doi:10.1038/ncomms9500
Received
07 March 2015
Accepted
28 August 2015
Published
12 October 2015

Abstract

Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and ‘binned’ responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a ‘bio-litmus’ in a manner read by simple optical means.

At a glance

Figures

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  1. Nano-guided cell networks for processing molecular information.
    Figure 1
  2. Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.
    Figure 2
  3. Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.
    Figure 3
  4. Affinity-based probing for functional analysis of AI-2-induced protein expression.
    Figure 4
  5. Single and multi-population cell responses to autoinducer-2.
    Figure 5
  6. Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.
    Figure 6
  7. Extension of nano-guided cell networks for hypothetical regulatory structures.
    Figure 7

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Introduction

It has become increasingly apparent that a wealth of molecular information exists, which, when appropriately accessed, can provide feedback on biological systems, their componentry and their function. Thus, there is a developing niche that transcends length scales to concurrently recognize molecular detail and at the same time provide understanding of the overall system1, 2. An emerging scheme is to develop nano- to micro-scaled tools that intimately engage with biological systems through monitoring and interacting at the molecular level, with synthetic biology being one such tool3, 4, 5, 6, 7.

While synthetic biology is often viewed as an innovative means for ‘green’ product synthesis through the genetic rearrangement of cells, their biosynthetic capabilities and their regulatory networks can instead be tuned for executive function8, 9, 10. That is, cells can be rewired to survey molecular space3, 11, 12 as they have sophisticated capabilities to recognize, amplify and transduce chemical information13. Further, they provide a means to connect biological systems with traditional microelectronic devices and in doing so present a potential interface between chemically based biomolecular processing and conventional vectors of information flow, such as electrons and photons14, 15, 16. Specifically, through engineered design, cell-based molecular processing can be further coupled to enable external abiotic responses. Cells, then, represent a versatile means for mediating the molecular ‘signatures’ common in complex environments, or in other words, they are conveyors of molecular communication17, 18, 19.

Further, beyond clonal cell-based sensors, there is an emerging concept of population engineering to establish microorganisms in deliberate networks that enable enriched system identification through a combination of distinctive yet coexistent behaviours, including, perhaps, competitive or cooperative features8, 20, 21, 22, 23, 24, 25. We posit the use of cell populations assembled in parallel¸ where multiple microbes with distinct molecular recognition capabilities work congruently. An advantage is that populations, as opposed to few cells, can facilitate thorough sampling since the presence of many cells increases their spatial breadth and per-cell data contributions (Fig. 1a). Each cellular unit undergoes independent decision-making and contributes a datum to its entire constituency. The prevalence of data provided within the population, then, substantiates a collective output by the system based on the molecular landscape. As follows in a multi-population system, molecular input thus influences the outcomes of each population, and elicits plural responses when the molecular input ranges overlap the ranges of the sensing populations21, which can define classification boundaries (Fig. 1a). Cell-mediated classification was posited in silico by Didovyk et al.21, where reporter libraries with randomized sensitivities to a molecular cue elicit concentration-dependent fluorescent patterns and these are elucidated by population screening . In the present construct, multiple populations enable multiplexed analysis, resulting, here, in a response gradation that is designed to index the molecular input ‘signature’. Consequently, the fed-back information becomes transfigured beyond a dose-dependent cell-by-cell analysis. That is, the output is predicated by the comparison between the populations rather than accumulation of response within a total population.

Figure 1: Nano-guided cell networks for processing molecular information.

Nano-guided cell networks for processing molecular information.

(a) Biotic (multicellular) processing is facilitated by cell recognition, signal transduction and genetic response. The genetically encoded response reflects the identity and prevalence of the target molecule(s). Biotic processing includes both increased cell number of responders and their genetically tuned response patterns. (b) Abiotic processing, used in conjunction with biotic processing, adds dimensionality to cell-based output by modifying through a physical stimulus (in our example, magnetic focusing). (c) Schematic of a cell population and nanomaterial-based network comprising both biotic (green/red axis) and abiotic (black axis) processing mechanisms. This conceptual system interprets molecular information by intercepting diverse molecular inputs, processes them autonomously through independent cell units within the system and refines output to include positive responders that are viewed via orthogonal means (visual classification). The system’s hierarchical structure allows molecular information to be refined into categorized collective outputs.

With population engineering as a premise for enriched molecular information processing, we engineered cell species, each to achieve an appropriate output through genetic means. There is conceptual basis for incorporation into networks, such as through mobile surveillance and position-based information relay26, 27. Hence, it is conceivable that, in addition to autonomous molecular recognition and processing afforded by synthetic biology, the use of physical stimuli to enable cell response could confer similar networking properties28, 29. For example, the complete information-processing ‘repertoire’ can be expanded beyond specific cell responses by the integration of external stimuli that serve to collate cell populations30. Specifically, we envision integration of nanomaterials that enable co-responses to molecular inputs, such that cell populations employ traditional reporting functions, that is, fluorescence marker expression, as well as responses that enable additional processing via the integration of stimuli-responsive abiotic materials (Fig. 1b).

In our example, cells are engineered to respond by permitting the attachment of magnetic nanoparticles (mNPs), such that each fluorescent cell becomes receptive to a magnetic field. Thus, the combination of cell-nanoparticle structures provides further dimensionality for the conveyance of molecular information (via magnetic stimulation). That is, without magnetic collation the fully distributed system would harbour diffuse responses; a magnetically stimulated system results in acute output due to a filtering and focusing effect (Fig. 1b)31, 32, allowing binned information to be readily, and fluorescently, conveyed.

The detection and interpretation of signalling molecules in our example is based on a microbial communication process known as quorum sensing (QS). The molecules, autoinducers (AIs), are secreted and perceived within a microbial community; once accumulated, the AI level indicates that the population size has reached a ‘quorum’33, 34. By surpassing a threshold concentration, the AI signalling coordinates population-wide phenotypic changes35. We have designed a QS information processor that utilizes two cell populations to independently interrogate natural microbial communities and generate information about QS activity by accessing AI-2 (ref. 36). Each cell population becomes ‘activated’ in response to a characteristic AI-2 level by expressing a fluorescent marker and a streptavidin-binding peptide (SBP) on the outer membrane38. SBP provides a means for collating data by binding mNPs that are introduced into the community. Using a post-processing magnetic sweep, the system as a whole interprets a molecular landscape and refines output into colour-categorized, or ‘binned,’ states (no fluorescence, red, or red and green) through (1) parallel population processing and (2) acute focusing (Fig. 1c).

The use of engineered cells as data-acquiring units and selectively equipping each with functional nanomaterials to form a redistributable processing system merges two paradigms: decentralized, active probing at a molecular scale and self-organization of units through structured dependencies on stimuli42. The population-based system overall contributes categorized feedback about a biological environment.

Results

Surface expression of SBP and fluorescent protein fusions

First, we established expression of a fusion protein consisting of a fluorescent marker (enhanced green fluorescent protein (eGFP) and variants) and SBP. Importantly, for SBP to function as a coupling agent between cells and mNPs, we used AIDAc (kindly shared by J. Larssen)40 to export the chimeric protein to Escherichia coli’s outer surface. Translocation to a cell’s surface utilizes a signal peptide (for inner membrane translocation) and AIDAc as an outer membrane autotransporter pore38, 39, 40, 41, with the passenger protein linked to each. In Fig. 2a, we depict expression of three different constructs using Venus, eGFP and mCherry for optical transmission, and the AIDAc translocator domain for surface localization. These constructs are mapped inSupplementary Fig. 1. After induction with isopropyl B-D-1-thiogalactopyranoside (IPTG), cultures were probed for surface expression of the SBP portion of the tagged fluorescent protein. Cells were incubated with fluorescently labelled streptavidin; the fluorophore of the streptavidin probe was orthogonal to the expressed fluorescent protein. The multiple fluorescence emissions were analysed by confocal microscopy without spectral overlap. The fraction of cells (fc) that exhibit colocalized fluorescent protein and the fluorescently-labeled streptavidin is reported in Fig. 2b, showing that SBP–Venus cells bound streptavidin at a slightly lower frequency than SBP–mCherry and SBP–eGFP, which exhibited statistically similar fractions (fc=0.7).

Figure 2: Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

(a) A T7 cassette was used to express chimeric proteins consisting of a membrane autotransporter domain (AIDAc), one of several fluorescent proteins and a streptavidin-binding peptide (SBP). Fluorophore-tagged streptavidin (SA) was used to bind SBP. (b) Of cells expressing fluorescent proteins (FP), those also marked by SBP coupling are represented as a ‘colocalized fraction (fc),’ plotted with image analysis-based s.d. of at least five replicates. The asterisk ‘*’ denotes fc that +SBP–eGFP and+SBP–mCherry are statistically equivalent (fc~0.7) by t-test and greater than +SBP–Venus cells. (c) Composite images show cell fluorescence (Column I) from the fluorescent protein (FP); labelled streptavidin using orthogonal filter sets (Column II); and an overlay of both (Column III). Arrows indicate representative cells with strong colocalization. Plotted in Column IV are the fluorescence mean grey values (y-axis) from a representative horizontal slice of the composite image (x-axis). Vertical bars displayed between Columns III and IV identify the position of each analysed slice. Arrows indicate peaks that match the highlighted cells in Column III. fc values are noted. Fluorophores with non-overlapping spectra were paired. Row 1, Venus expression (yellow-green) was paired with Dylight405-labelled SA (blue). Row 2, eGFP expression (green) was paired with Alexafluor594-labeled SA (red). Row 3, mCherry expression (red) was paired with Alexafluor488-labeled SA (green). Scale bar in lower left, 50μm.

That is, microscopy results related to the colocalization analysis are depicted for pairings of Venus and blue-streptavidin (SA), eGFP and red-SA, and mCherry and green-SA (Fig. 2c). Strong signals were observed in both filter sets (the fluorescent protein (Column I) and the labelled streptavidin (Column II)). Overlaying each image reveals colocalization, as indicated in Column III, where arrows point to examples of strong colocalization. In addition, Column IV plots fluorescence intensities across horizontal sections of the images, where cells that exhibit colocalized fluorescence are indicated by superimposed peaks. For +pSBP–Venus cells, those with both a blue and yellow signal are observed as pale blue–violet in the overlaid image. Cells with +pSBP–eGFP and +pSBP–mCherry and labelled streptavidin emit both green and red signals; their colocalization appears yellow. Controls shown in Supplementary Fig. 2, verify that fluorescent streptavidin (all colours) has specificity for only SBP-expressing cells over negative controls. Colocalization indicates that not only are both components of the fusion, SBP and the fluorescent protein, expressed, but that SBP is accessible to bind streptavidin on the cell’s surface. This is the first use of AIDAc for cell surface anchoring of fluorescent proteins, each having been functionalized with an affinity peptide.

Cell hybridization via mNPs

Given that expression of a fluorescent protein tagged with SBP enabled external binding of streptavidin, we employed this interaction for fastening streptavidin-functionalized materials directly to the cell surface. We chose streptavidin-conjugated mNPs, 100nm in diameter (an order of magnitude smaller than a cell), for binding to a cell surface (Fig. 3a) to impart the abiotic magnetic properties. Scanning electron microscopy (SEM) was used to observe surface interaction between cell surface-expressing SBP and streptavidin-functionalized mNPs. Supplementary Fig. 3a,bshows electron micrographs of E. coli cells (dimensions 1.5–2μm in length) and the mNPs (~100nm in diameter). The SEM image in Fig. 3b, shows a magnetically isolated SBP-expressing cell with streptavidin-mNPs. The sample was prepared by mixing SBP-expressing cells with streptavidin-mNPs, then collecting or ‘focusing’ into a magnetized pellet via magnetic field, then separating from unbound cells in the supernatant. The cells were then washed and resuspended. In Fig. 3b, clusters of surface-bound mNPs are observed. In addition, the elemental composition was analysed with energy-dispersive X-ray spectroscopy, shown in Fig. 3c by an element map superimposed with carbon (red) and iron (green). While the cell appears to be of a uniform carbon composition, the particles localized at the cell surface (highlighted with arrows) were found having a strong iron composition; thus, elemental analysis confirmed particle identity as iron oxide mNPs. Additional characterization of magnetic functionality, including detailed SEM and fluorescent microscopic analysis prior to and after application of magnetic fields, is described in theSupplementary Information (Supplementary Fig. 3).

Figure 3: Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

(a) Cell surface binding of streptavidin-conjugated magnetic nanoparticles occurs via surface-anchored streptavidin-binding peptide (SBP). The fusion of T7-expressed SBP-fluorescent protein (FP)-AIDAc enables the cell surface accessibility. (b) Scanning electron micrograph of an E. coli cell with surface-bound particles. (c) Element map of carbon (red) and iron (green) through energy-dispersive spectroscopy.

In sum, the well-known affinity interaction between streptavidin and the peptide SBP is harnessed to endow cells with non-natural abiotic properties. Here coupling a functionalized nanomaterial to the surface-displayed peptide physically extends the fusion protein and also adds physical (magnetic) functionality to the cell.

Linking expression to AI-2 recognition

The expression system for pSBP–Venus was then put under AI-2 control so that the protein is expressed in the presence of AI-2 instead of IPTG. That is, we coupled the native QS signal transduction circuitry to the reporter cassette. To ensure ample expression (as the native operon is fairly weak), we placed expression of T7 RNA polymerase under control of the natural QS circuitry43. Phosphorylated AI-2 activates the system through derepression of the regulator LsrR, naturally upregulating AI-2 import and phosphorylation44, and, by design, the T7 RNA polymerase on a sensor plasmid43. When sbp–Venus is included downstream of a T7 promoter region on a second plasmid, expression is then triggered by AI-2 uptake (Supplementary Fig. 4a). Then, we used two host sensor strains engineered to provide varied AI-2 sensitivity (denoted responders ‘A’ and ‘B’). In ‘A’, lsrFG, genes required for internally phosphorylated AI-2 degradation45, 46 are deleted. Also, both strains lack the terminal AI-2 synthase, luxS, so they cannot produce AI-2 and, instead, must ‘receive’ AI-2 from an external source (Supplementary Fig. 4a). The phenotypic difference between A and B is the threshold level of AI-2 that activates the genetic response47, 48. Fully constructed, these cells are designed to take up and process AI-2 to generate fluorescence output (that co-functions with streptavidin binding).

We next evaluated the kinetics of surface-fusion protein expression and effects on cell growth. The AI-2-induced expression for AIDAc-linked and SBP-tagged fluorescent proteins did not alter growth kinetics for either cell type (Supplementary Fig. 4b,c). Expression efficacy was also evaluated via immunoassay of the outer membrane, probing for AI-2-induced surface display. After induction with 20μM AI-2, extracts from cell types A and B were size-separated and blotted using alkaline phosphatase-conjugated streptavidin to probe for the SBP-tagged protein fusion (Supplementary Fig. 5). The 88kDa AIDAc–Venus–SBP protein was only found in the membrane-containing pellet fraction (Fig. 4a). Analogously, protein orientation was assessed by immunolabeling the fluorescent protein. Cell type B transformed with pSBP–eGFP was induced with 20μM AI-2 overnight; cell surfaces were then probed for eGFP using a mouse anti-GFP primary antibody and red-labelled secondary anti-mouse IgG. Simultaneously, cells were observed using phase contrast and fluorescence confocal microscopy. We noted a punctate pattern for eGFP, which was in one-to-one correspondence with red immunostaining of the surface-expressed protein. The positive staining of eGFP-expressing cells for red fluorescence, contrasted by the absence of negative control immunostaining indicated surface exposure of the fusion (Supplementary Fig. 6). Confocal microscopy confirmed precise colocalization of the eGFP and red-labelled antibodies within the confines of individual cells (Fig. 4b). Therefore, efficient transport of this functionality to the membrane under AI-2 induction was demonstrated in each host.

Figure 4: Affinity-based probing for functional analysis of AI-2-induced protein expression.

Affinity-based probing for functional analysis of AI-2-induced protein expression.

(a) 64–82kDa region of western blot for pelleted (P) and supernatant (S) protein fractions isolated from Type A and B cells. Alkaline phosphatase-conjugated streptavidin was used to target AIDAc–Venus–SBP at expression timepoints. Arrows indicate the expected position of the full fusion protein. (b) Immunostaining for assessment of the fluorescent protein surface accessibility. The external surfaces of cells expressing AIDAc–eGFP–SBP were probed with an anti–eGFP and Alexafluor594-labelled antibody pair. A representative overlaid fluorescence and phase contrast image is shown along with fluorescence images of the green (G) and red (R) filters for the boxed-in region. Scale bar, 2μM.

Establishing molecular ranges for cell interrogation

Importantly, the engineered cells each provide a characteristic response to the level of AI-2. Recently, we showed that AI-2 level influences the quorum size of responding engineered populations but does not alter the expression level within each quorum47. Here we evaluated our engineered AI-2 responders, again for quorum size (or in other words, percentage of AI-2-responsive cells in the population), this time varying the compositions of molecular input and the configuration of responders (Fig. 5a). First, we added AI-2, synthesized in vitro, to each of the two responder populations (Fig. 5b). We also added conditioned medium (CM), the spent medium from an AI-2 producer culture containing metabolic byproducts, as well as AI-2 (refs 36, 49; Fig. 5c). We also mixed the responder populations and added AI-2 to gauge responses in complex cultures (Fig. 5d).

Figure 5: Single and multi-population cell responses to autoinducer-2.

Single and multi-population cell responses to autoinducer-2.

(a) Fluorescence output is linked to small molecule input, derived from purified or crude sources. Fluorescence from Responders A and B was analysed after exposure to autoinducer-2 (AI-2) in mono and mixed culture environments. (b) Venus expression from in vitro-synthesized AI-2 added to monocultures of A and B. (c) Venus expression from conditioned media (CM) added to monocultures of A and B. CM was isolated from WT W3110 E. coli cultures sampled at indicated OD. Data are averages from triplicate cultures with s.d. indicated. (d) Red and green fluorescence responses to AI-2 during co-incubation of Responders A (pSBP–mCherry+, red) and B (pSBP–eGFP+, green). Representative fluorescence images show colocalization of red and green cells. Scale bar, 10μm. The average cell count per responder cell is plotted against AI-2 concentration, as determined by image analysis in quadruplicate. All data are plotted as averages of at least triplicate samples with s.d.

Specifically, in Fig. 5b, A and B populations were incubated at mid-exponential phase with in vitro-synthesized AI-2 (refs 50, 51) at concentrations: 0, 2, 10, 28 and 75μM. After 12h, samples were observed for fluorescence by confocal microscopy and then quantified by fluorescence-activated cell sorting (FACS; Supplementary Fig. 4c). We found that SBP–Venus expression for responder A cells occurred at the lowest tested level (2μM AI-2), where 56% of the population expressed SBP–Venus and this fraction increased with AI-2 reaching a maximum of 90% at 28μM. For type B, a more gradual trend was found; only ~1% was fluorescent from 0-2μM, and this increased from 9 to 46% as AI-2 was increased to 28μM. Finally, the highest fraction of fluorescing cells was found at the highest concentration tested, 75μM.

We next isolated CM, which contains a dynamic composition of unfiltered metabolites and media components, from W3110 E. coli cultures at intervals during their exponential growth, throughout which AI-2 accumulates (AI-2 levels for the samples are indicated in Supplementary Fig. 7). CM aliquots were mixed with either A or B cells and cultured in triplicate for 12h. Through FACS analysis it was found, again, that a larger subpopulation of A expressed Venus compared with population B at any concentration (Fig. 5c). Statistically relevant expression from B was not apparent until incubated with CM from cultures at an optical density (OD) of 0.23. In all cases, population A recognized AI-2 presence, including from media isolated at a W3110 OD of 0.05, the minimum cell density tested in this study.

The sensitivities of both strains to AI-2-mediated induction corroborate previous literature10, 47. These trends demonstrate that strains engineered for altered sensitivity to molecular cues provide discrimination of concentration level. That is, the identical plasmid expression system was transformed into different hosts, providing robust and distinct levels of expression.

Having developed cell types A and B with differential ability to detect AI-2, we next altered the reporters so that each cell type expressed a unique SBP-fluorescence fusion for colour-coded designation. Cell type A was engineered with pSBP–mCherry and type B with pSBP–eGFP, resulting in red and green fluorescence, respectively. These populations were mixed together in equal proportion at mid-exponential phase, introduced to a range of AI-2 concentrations, and incubated overnight. Populations A and B exhibited equal growth rates when cultured alone and together (Supplementary Fig. 8c); it followed that the cocultures should comprise a 1:1 ratio of each constituent. Fluorescence output is shown by representative images in Fig. 5d. Also in Fig. 5d, the green and red cell count is plotted from a quadruplicate analysis for each input concentration.

Coculturing enables parallel processing as the molecule-rich environment is perceived by each cell, and is processed uniquely per cell type. Yet, since each sensing mechanism is a living and proliferating population, we tested whether the potentially altered dynamics of coculturing would permit the same sensitivities as isolated culturing. We evaluated the Monod-type saturation constant for each population independently and in cocultures. We found, in Fig. 5d, the general trends in response to an increasing AI-2 level were as predicted by modelled response curves (Supplementary Table 4), which were also well-correlated to Fig. 5b data (Supplementary Fig. 8a,b). That is, the saturation constants that describe dependence on AI-2 were unchanged when measured in cocultures. Phenomenologically, as expected, an initial accumulation of red type A responders was found. Then, at higher AI-2 levels, we found an emergence of a green subpopulation (type B). Above 28μM, there was no longer an apparent differential response that would otherwise enable discrimination of AI-2 concentration; based on the consistency with modelled behaviour, coculturing contributed to dampen the response as the maximum percentage of responding cells in cocultures is 50% instead of 100%. However, the overall fluorescence output is enriched by the combination of multiple populations since the ranges of sensitivity overlap and effectively expand that of the master population (Supplementary Fig. 8d). Specifically, because the fluorescence of B is described by a larger saturation constant, its fluorescence continually increases at higher AI-2 concentrations, while the fluorescence of A remains unchanged. Thus, coculturing between A and B enables resolvable output that is lower than the detection limit of B (due to A) yet surpasses the upper limit at which A saturates by the inclusion of B. The choice to fluorescently differentiate A and B was important because the output would otherwise be biased by extracellular components including the existence of non-sensing cells. Due to colour designation of A and B, a colour ‘pattern’ emerges as a feature of the parallel response, which we recognize is independent of the absolute fluorescence of the population.

Consensus feedback through multidimensional processing

We hypothesized that the value of cell-based sensing would be enhanced if the cell output could be collated in an unbiased manner that in turn were easily ‘read’ using optical means. We engaged magnetic processing, which represents an abiotic processing step that enhances the signal by focusing the collective response. Hence, cells were equipped with streptavidin-conjugated mNPs (Fig. 3). The ability of a magnetic field to refine fluorescence output through filtering and focusing is described in the Supplementary Information (Supplementary Fig. 11). Thus, in our combinatorial approach, fluorescence feedback about molecular information within a microbial community entails biotic processing through constituencies of two independent cell types in conjunction with magnetic post-processing that is enabled by guidance at the nanoscale (Fig. 6c). Moreover, since the fluorescence feedback data is provided through two constituencies, consensus from each independently provides an aggregate output; in our example, the output becomes relayed as a distinctive ‘binned’ category due to finite colour-combinations generated from constituencies A and B (Fig. 6c).

Figure 6: Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

(a) A and B cell types were co-incubated with AI-2 levels ranging from 0 to 55μM AI-2 (left axis), then imaged after magnetic nanoparticle coupling and magnetic collation. Fluorescence results (centred directly over the magnet) are shown from high to low input (top left to bottom right). (b) Quantification of red and green fluorescence cell densities per AI-2 level. (c) The process of accessing molecular information begins by distributing Responders A and B within the environment of an AI-2 producer, P. A and B independently express fluorophore fusions and are linked with magnetic nanoparticles on processing autoinducer-2. Magnetic focusing translocates fluorescing responders. Image analysis of the magnetically collated cell aggregate reveals classified fluorescence output, representing the AI-2 composition of the interrogated environment. (d) Bright field (left) and fluorescence (right, red and green filters) images positioned over the edge of a magnet, as indicated by the inset. The sample in the bottom image pair was isolated from an environment of low AI-2 accumulation. The sample in the top image pair was isolated from a high AI-2 environment. (e) Quantification of visual space occupied by collated cells (eGFP and mCherry expressers) while distributed (- magnet) and magnetically focused (+). Scale bars, 50μm.

Again, type A transmits red output (SBP–mCherry+) and type B transmits green (SBP–eGFP+). These were first co-incubated with titred concentrations of AI-2, to obtain results similar to those ofFig. 5d. By coupling mNPs to the responsive parallel populations, we tested for aggregate two-colour output to provide informative feedback within a set of outcomes ranging from no colour, red-only to red+green. After overnight co-incubation and a magnetic sweep with streptavidin-mNPs, fluorescence results are shown in Fig. 6a, where the recovered cells are displayed above a magnet’s center in order from highest to lowest AI-2 level (top left to bottom right). The processing output generated by the range of conditions was quantitatively assessed for contributions from A and B responders. The spatial density of each fluorophore, or the area occupied by fluorescent responders as a percentage of total visible area, was quantified and plotted in Fig. 6b. Here the trend of increasing fluorescence with AI-2 is followed by both A and B cell types; however, red A cells accumulate at a higher rate than green B cells. This relationship between A and B processing is not only consistent with their previous characterizations (Fig. 5) but indicates that the aggregate output is unbiased regardless of assembly with mNPs and magnetic-stimulated redistribution (Supplementary Information, Supplementary Fig. 14a).

Next, A and B cells were added together to probe the QS environment of Listeria innocua, an AI-2-producing cell type that is genetically and ecologically similar to the pathogenic strain L. monocytogenes52. The environment was biased towards low and high cell density conditions by altering nutrient levels to develop contrasting scenarios of AI-2 level. Preliminary characterization in the Supplementary Information indicated that L. innocua proliferation is unperturbed by the presence of E. coli responders (Supplementary Fig. 12) and that type A cells detect AI-2 at lowListeria densities limited by sparse nutrients; then with rich nutrient availability, cell proliferation permits a higher AI-2 level that can be detected by type B (Supplementary Fig. 13). Replicating these conditions, we expected red fluorescence to be observed at low culture density and for green fluorescence to be reported when high (Fig. 6c). Two conditions were tested: L. innocua was proportioned to responder cells at 20:1 in dilute media to establish a low culture density condition or, alternatively, a ratio of 200:1 in rich media for a high culture density condition. After overnight co-incubation and a magnetic sweep (applied directly to the triple strain cultures) with streptavidin-mNPs, the recovered cells are displayed above a magnet’s edge (shown in Fig. 6d). Acute focusing of the fluorescence signals, contributed by each subset population of the processor (A and B), is visually apparent. The magnetic field had a physical effect of repositioning the ‘on’ subsets to be tightly confined within the magnetic field.

The processing output generated by the contrasting culture conditions was again assessed for the respective contributions of A and B, and for changes in spatial signal density due to the magnetic sweep (Fig. 6e). The analysis was based on images provided in Supplementary Fig. 14b. Data inFig. 6e indicate that red type A cells are prevalent regardless of culture condition (except negative controls). However, compared with the low AI-2 condition, the abundance of green cells is 100-fold higher in the high AI-2 condition. In addition, the ratio of green to red was consistent prior to and after magnetic concentration, substantiating observations in the distributed system. Further, data show that magnetic refining increased per-area fluorescence 100-fold or 10-fold in low and high cell culture studies, respectively.

Based on the thresholds established for responder populations A and B, we found colour-coded binning corresponded to AI-2 level, where ‘red-only’ represented less AI-2 than ‘red+green’ (Fig. 5d). Thus, we found a binned output was established via this multidimensional molecular information-processing system and that this matched the expectations. Red feedback (from responder A) indicated dilute AI-2 accumulation occurred in the low density culture. In the dense cultures, high AI-2 accumulation turned on both A and B for combined red and green feedback.

System response patterns defined by parallel populations

Our example demonstrates the concept of an amorphous processing system that utilizes several biotic and abiotic components for multidimensional information processing. Interestingly, a binning effect was enabled: our system yields an index of colour-categorized feedback that characterizes the sampled environment. In Fig. 7, we present a means to extend our approach to multidimensional systems, those with more than one molecule-of-interest and at different concentrations. That is, by appropriate design of the cell responders, we can further enrich the methodology, its depth and breadth of applicability. We depict 10 hypothetical pairs of responses (with defining equations located in Supplementary Table 5)—those that can be driven by appropriately engineering cells to portend altered genetic responses. For example, rows 1 and 3 provide genetic outcomes as a function of analyte (AI-2) concentration. The hypothetical depictions are feasible as ‘designer’ signal transduction and marker expression processes enabled by synthetic biology21, 53, 54. Rows 2 and 4 demonstrate the corresponding visual planes, where red cell numbers (x-axis) are plotted against green (y-axis), illustrated by the first example. If one divides the two-dimensional space into quadrants (no colour, majority red, majority green, and equivalent ratios of red and green), it becomes apparent that the relationship between cell types influences the ‘visual’ or optical output. Thus, the 10 arbitrary response sets yield a variety of pairings that can provide unique visual patterns for categorizing molecular information. We have simplified the analysis by placing dot marker symbols at the various coincident datapoints, revealing visual patterns. In this way, the ability to incorporate unique responses to a multitude of molecular cues, all within a single pair of cells, or through further multiplexing with additional cell populations becomes apparent.

Figure 7: Extension of nano-guided cell networks for hypothetical regulatory structures.

Extension of nano-guided cell networks for hypothetical regulatory structures.

(a) Rows 1 and 3 depict 10 hypothetical genetic responses to molecular inputs for pairs of fluorescence-reporting cell populations (red, R and green, G). Rows 2 and 4 depict genetic responses as phase-plane plots yielding distinct patterns. This establishes a visual field, showing the extent of any population–population bias (illustrated in example case 1). (b) Left panel: a two-population pairing (shown in case 10) defines visual output that inherently bins into three quadrants: Q1, negligible colour; Q2, red bias due to majority red cell output; and Q4, combined red and green output. Right panel: data from Figs 5d and 6bare plotted analogously, where each data point represents an autoinducer-2 input (labelled, μM). As expected, red and green outputs were binned into Q1, Q2 and Q4 as indicated by coloured outlines.

Our AI-2-conveying cell network is similar to example 7 in Fig. 7a and the AI-2 response curves inFig. 5 (characterized by Supplementary Table 4 equations). Example 7 establishes output into three basic quadrants, including Q1 (negligible colour), Q2 (majority red) and Q4 (roughly equal red and green) (Fig. 7b). We recast the data from Figs 5d and 6b as a phase-plane portrait in Fig. 7c. This reveals the mechanisms by which the output is binned and how the originating cell response curves lead to this pattern, which in turn, was unchanged due to magnetic refinement. InSupplementary Fig. 15, we demonstrate a parameterization of the red and green response curves that suggest the methodology is robust, that when cells are appropriately engineered one could ‘tune’ system characteristics to enhance or diminish a binning effect. We suggest that the utility of subcellular genetic tuning extends well beyond per-cell performance. Rather, we suggest such a strategy may be used to guide the dynamics of population architecture for actuation of by-design response patterns at a systems level.

Discussion

While cell-based sensors work well in well-defined assay conditions, extension to complex environments remains a challenge. They grow, they move, they perturb their environs, they report in a time and concentration-dependent manner, small numbers of sensor cells may require signal amplification and so on. Also, increasingly, bacterial cells are engineered for user specified ‘executive’ functions in complex environments55, 56, 57. Their performance depends on their ability to filter out extraneous noise while surveying the molecular landscape, and providing informed actuation.

Our system interrogates the molecular space by focusing on bacterial QS and a widely distributed signal molecule, AI-2. In addition to genetic attributes of the AI-2-responding sensor cells, AI-2 is a chemoattractant for E. coli, and hence E. coli engineered to sense and respond to AI-2 will naturally move towards its sources, enabling full sampling of the prevailing state10, 37. Each strain evaluates AI-2 with a distinct sensitivity. When ‘activated’ in response to a characteristic level, the cells simultaneously expressed a fluorescent marker and a SBP on the outer membrane via AIDAc translocation. SBP provides a means for cell hybridization through its strong affinity to streptavidin, and here, aids in binding mNPs. This enables the non-genetically coded property of cell translocation within a magnetic field through physically stimulated focusing and binning.

By making use of a diversity of biotic and abiotic features, our multidimensional system of ‘responder’ populations exemplifies several key metrics that promote executive performance in such environments: active molecule capture, post-capture refining of the detection output and finally the utilization of multiple feedback thresholds58, 59, 60. Here cells facilitate AI-2 recognition autonomously and actively because, as a distributed network they reside planktonically, chemotaxing to and continually processing signals over time. When AI-2 is detected, a processor cell’s cognate machinery responds by upregulation of the native QS operon, leading to rapid signal uptake and thereby creating an active-capture signal-processing mechanism. To maximize information acquisition and account for a potentially heterogeneous molecular landscape, cells serve as molecular sampling units among a distributed population, which leads to data fed back as a consensus of fluorescent ‘datapoints’. Then, distributed data collection can be selectively reversed via the incorporated abiotic feature: mNPs, fastened externally on the cell through affinity-guided self-assembly. As such, responding cells obtained this extendable feature, thereby becomes sensitized to repositioning within a magnetic field.

The layered nature of the processor here, from the subcellular to multicellular scale, permits a series of selective steps: it commences with the AI-2-triggered expression cascade which releases a tight repressor, surface localization of both the fluorescent protein and SBP tag, and finally nanoparticle binding for recovery. In addition, multiple layers of amplification result in orthogonal fluorescence feedback. The AI-2 detection event leads to whole-cell fluorescence through expression of many protein copies47. Then their physical collection further amplifies the signal, yielding a macroscopic composite of many individual cell units. When utilized as a network of multiple constituencies, responder cell types A and B contribute individual recognition results (off, red or green) to a single consensus output. Finally, due to their overlapping thresholds for recognition of the same molecule, in this case, AI-2, parallel processing by A and B responders can contribute to visual interpretation of information about the molecule. Outcomes are classified into a finite number of states: here output to no fluorescence, red, or red and green, with each addition of colour as a metric of a higher interval of AI-2. In many respects, the elucidation of layered information networks as demonstrated here is analogous to computer information processing via information theory61, 62, 63.

Here, however, interrogation of biological systems requires a reliable means for accessing molecular information—that which is communicated between biological species and that which can be relayed to the end user. The responder cells need not be present in high concentration, nor must they all be collected in the present format. We suggest that engineered biological mechanisms are well-poised to serve at this critical interface between information acquisition and user interaction. Thus, the functional design of components for autonomous self-assembly, decision-making and networking is requisite in the field of micro- and nano-scaled machines. Our combinatorial approach allows for cells to independently assess, yet collectively report, on molecular information. Its processing is enabled through appropriate integration of synthetic biology and nanomaterials design. We suggest this approach provides a rich opportunity to direct many formats of multi-population response through genetic tuning and systems-level engineering. Further development of cellular networks and incorporation of alternate abiotic attributes can expand the depth and breadth of molecular communication for user specified actuation.

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Nanoparticles Could Boost Effectiveness of Allergy Shots

Reporter : Irina Robu, PhD

Immunotherapy is a preventive treatment for allergic reactions to substances such as grass pollens, house dust mites and bee venom. The only existing therapy that treats their causes is allergen-specific immunotherapy or allergy shots which can cause severe side effects. For many people, allergies are a seasonal annoyance. But for others, exposure to a particular allergen can cause antagonistic reactions such as itching, breathing problems or even death. Allergy shots can diminish sensitivity by gradually ramping up exposure to the offending substance. Each allergy shot contains a tiny amount of the specific substance or substances that trigger your allergic reactions.

Holger Frey and colleagues report in Biomacromolecules the development of a potentially better allergy shot that uses nanocarriers to address these unwanted issues. In order to develop a safer, cause-based therapy scientist have developed nanoparticles that enclose an allergen and deliver it to specific cells. However, these nanocarriers degrade slowly, hindering the efficiency of the treatment.

Nanocarriers offer the following potential advantages: site-specific delivery of drugs, peptides, and genes, improved in-vitro and in-vivo stability and reduced side effect profile. However, nanoparticles are usually first picked up by the phagocytic cells of the immune system which may promote inflammatory disorders. In order to overcome the limitations, the researchers designed a novel type of nanocarrier created on the biocompatible molecule poly (ethylene glycol) that releases its cargo only in targeted immune cells.

This approach could be used not only for allergies but also can be used for other immunotherapies such as cancer and AIDS.

Source

https://www.eurekalert.org/pub_releases/2015-09/acs-ncb092215.php

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Extraordinary Breakthrough in Artificial Eyes and Artificial Muscle Technology

Reporter: Irina Robu, PhD

Metalens, flat surface that use nanostructures to focus light promise to transform optics by replacing the bulky, curved lenses presently used in optical devices with a simple, flat surface.

Scientists at the Harvard John A. Paulson School of Engineering and Applied Sciences designed metalens who are mainly focused on light and minimizes spherical aberrations through a dense pattern of nanostructures, since the information density in each lens will be high due to nanostructures being small.

According to Federico Capasso, “This demonstrates the feasibility of embedded optical zoom and auto focus for a wide range of applications, including cell phone cameras, eyeglasses, and virtual and augmented reality hardware. It also shows the possibility of future optical microscopes, which operate fully electronically and can correct many aberrations simultaneously.”

However, when scientists tried to scale up the lens, the file size of the design alone would balloon up to gigabytes or even terabytes. And as a result, create a new algorithm in order to shrivel the file size to make the metalens flawless with the innovation currently used to create integrated circuits. Afterward, scientists follow the large metalens to an artificial muscle without conceding its ability to focus light. In the human eye, the lens is enclosed by ciliary muscle, which stretches or compresses the lens, changing its shape to adjust its focal length. Scientists at that moment choose a thin, transparent dielectric elastomer with low to attach to the lens.

Within the experiment, when the voltage is applied to elastomers, it stretches, the position of nanopillars on the surface of the lens shift. The scientists as a result show that the lens can focus instantaneous, control abnormalities triggered by astigmatisms, and achieve image shift. Since the adaptive metalens is flat, you can correct those deviations and assimilate diverse optical capabilities onto a single plane of control.

SOURCE

https://news.harvard.edu/gazette/story/2018/02/researchers-combine-artificial-eye-and-artificial-muscle

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Original Tweets Re-Tweets and Likes by @pharma_BI and @AVIVA1950 at #kisymposium for 17th annual Summer Symposium: Breakthrough Cancer Nanotechnologies: Koch Institute, MIT Kresge Auditorium, June 15, 2018, 9AM-4PM

 

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SYNOPSIS – 17th annual Summer Symposium: Breakthrough Cancer Nanotechnologies: Koch Institute, MIT Kresge Auditorium, June 15, 2018, 9AM-4PM

 

 

https://kochinstituteevents.cvent.com/events/Registrations/MyAgenda.aspx?i=c46642c6-abb0-4f3b-97b7-ae1bb167f304&sw=1

Announcement

Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in

REAL TIME

@pharma_BI

@AVIVA1950

 

All TWEETS from LPBI’s Twitter.com handles at

 

  • Friday, June 15, 2018
8:00 AM – 9:00 AM Registration/Check-In

 

9:00 AM – 9:10 AM Introductory Remarks: Tyler Jacks and Sangeeta Bhatia

Speakers:

o   Sangeeta Bhatia,

  • Challenge meet Opportunity – Future Cancer Research Priorities
  • Prevention and early detection of Cancer for improved outcomes
  • Global cancer burden – Cancer diagnosis in Low-resources settings
  • 2000 microchip became nanoscale – other materials in nanoscale: 1994 – Present advancement in material and devices

o   Tyler Jacks

  • Nanotech, Diagnostics, Therapeutics, Cancer Care, Cancer Biology
  • New Center for NanoMedicine @MIT aka, @MIT.NANO
  • Sponsored: J&J, Sanofi, Thermo Scientific

 

9:10 AM – 10:40 AM Session I: Imaging and Diagnostics

·       Sanjiv Sam Gambhir, MD, PhD, Stanford University

Bubble Based Nanodiagnostics

  • Companies involved: Endra Inc, Bracco, Visualsonics
  • Canary Center Vision: Imaging: identify, isolate, Intervene
  • Value of early cancer detection: Survival is high ONLY in very very early vs tail of the distribution where 90% of funds goes for therapy: Prostate and Breast cancers — ARE detected early
  • Technology: Ultrasound Imaging ($1500 – low cost solution, for molecular level
  • Bubble based Nanodiognostics: Molecular level, gas pore shell made of lipids or albomin – provide information on location of cancer – molecular events, atomical modelity
  • Bubble size nanobubbles vs microbubbles targeted for Vascular Endothilium In vivo
  • Angiogenesis: KDR (molecule)/VEGFR2 (receptor)- over expressed only in neovascularized: Molecular targer is KDR – over expressed in ovarian and breast cancers
  • ability of bubbles to identify cancer, toxicity monitored , bubble arrive, bind, cleared
  • blind to histology – examine the binding, blind pathology
  • bubbles well correlated
  • histological diagnosis few mm to few cm — correlation of lesions benign
  • 1 cm lesion targeted present in KDR, normal tissue clears more rapidly vs in malignant tissue: ductal adenocarcenoma – 11 minutes after injection
  • Duration of US Molecular Imaging Signal
  • First-in-man – Bubble Transrectal US Photoaccustic detection modality
  • Enzyme activation nanobubbles – nano microbubbles to aggregate and create mass impact vs nanobubbles that are weak in signal potential
  • Synthesis of PA/US nanosize RF-acoustic imaging  – target Saline nanodroplets

·       Ralph Weissleder Developing Next Generation Diagnostics for Cancer @MGH

  • translational diagnostics: Precision oncology (1) Imaging (2) Tumor biopsy (3) Liquid biopsy
  • Enable earlier detection
  • Visualization for affordable cost
  • NEW Technologies at MGH with use of AI
  1. Rapid cellular protein profiling – Fine needle aspirates (FNA): DNA Barcode: Epitope – monoclonal antibodies: Sampling, Barcoding, Imaging, Analysis with AI: Pathways in single cells – protein level in different patient:

x axis patient number

y-axis: Protein type

Vesicles from Host vs from Tumor

2, Exosome

surface – Label-free detection and molecualr profiling of exosome : Pancreatic cancer detection – vesicle express  – they are heteroginous micro vesicles

3. POC testing (AI- Defraction Analysis)

Remote diagnosis:

  • Molecular diagnosis – 2015 (PNAS) – nano bids defract patterns – smart phone vs proprietary box – BioMed Eng
  • Algorithms – identify molecules and decision tree Clinical Trial at MGH: 24 Lymphoma patients, rest no-Lymphoma, higher precision than microspectrometry
  • Automated diagnosis – aspirate – subject to dioagnosis in the Box
  • From tissue to single cell
  • multiplex pathways
  • early detection
  • affordability
  • visualization/connectivity for interpretation

·       Angela Belcher New Approaches for Finding Tiny Tumors: Towards Early Detection and Treatment of Ovarian Cancer

  • Nano material and Biomaterial the intersection of
  • Genetic control of materials
  • Carbon nano tubes – Using Bacteriophage or phage – A virus that infect bacteria
  • from DNA to devices
  • Lincoln Labs + MGH + MIT – Carbon Nanotubes used in inexpensive diagnostics: Biomedical imaging: MI, PET: Optical imaging in vivo: Trade-fee: Resolution vs Depth
  • Ovarian Cancer: Minimal increase in overall survival over 30 years : Fallopian tubesmaximum reduction in tumor better survival rate
  • submillimiter detection: Carbon nanotube multiple tubes
  • Pre-surgical planning locates hard-to-detect ovarian tumor – find tumors that are hidden
  • Detection od Optically Luminescent – RT tracking T-cells in Cancer Immmunotherapy – following injection in mice remain for 2 days

Speakers:

·       Angela Belcher,

·       Sanjiv Sam Gambhir,

·       Ralph Weissleder

10:40 AM – 11:00 AM Coffee Break

 

11:00 AM – 12:30 PM Session II: Therapeutics

·       Mark Davis Designing Nanoparticles to Safely Cross the Blood-Brain Barrier for Treating Brain Cancers

  • Engineer particles for treating solid tumors
  • Intracellular drug delivery
  • 30-50nm
  • Improve PK properties
  • Limit Toxicity
  • Cyclodex
  • Interspecies translation – Nanoparticles can function to design in Humans
  • Combination of Avastin and nanoparticle component
  • PARP Inhibitor + CRLX101 – in clinical trial by AstraZeneka
  • PK in human been presicted if PK known in non-humans
  • Therapeutic escape from the exosome polymer end group chemistry
  • Tumor localization of Nanoparticles
  • Nanoparticles can function in Human NOT in the brain
  • better clinical trial design and combination drugs in small clinical trials
  • Brain primary vs mestasis in th ebrain
  • 50% HER2 positive will have metastesis in the brain
  • BBB TfP Receptor-mediate Transcytosis : Antibody affinity, monodenriate
  • Nanoparticles behave similarity to antibodies in the brain Nanoparticles characteristics: decreased
  • Improved Uptake of Nanoparticles  – fast release of NP during transcytosis
  • bring nanoparticles in combination therapy to the brain using transcytosis

·       Suzie Pun Modulating Tumor-Associated Macrophage

  • TAM – Targeting Tumor-associated macrophages
  • blood monocytes, immunosuppression, metastasis, invasion
  • Can we potentiated therapeutics delivery using TAM
  • wiin tumors, M2pep is internalized by TAMs
  • Cytotoxic KLA peptide – reduce inflammatory of the tumor – M2pepKLA reduces tumor growth rate and improves survival
  • increase avidity binding
  • Immunomodulation – Marophage targeting for
  • Targeting TAM for translation to Humans
  • improve drug potency
  • synthetic Nucleocapsids  —
  • Biomaterials for modulating tumor extracellular matrix
  • FSP integrates into fibrin, increasing its half-life – delay degradation of FSP-fibrin
  • Polymer cross linking – fibrin deposition in brain metastases
  • Fibrin stabilization by FSP alters TAM chronic FSP treatment increases brain metastasis

·       Daniel Anderson Nanoparticle Formulations for RNA Therapy and Gene Editing

  • can we make drugs to repair our DNA for therapy
  • barriers for systemic delivery of nanaoparticles
  • RNA THERAPEUTICS sIRNA – interference: Turning Genes Off: Modular Pharmacology: sequence Selection, Chemical Modification, Encapsulation (like artificial viruses)
  • What material can be used for RNA delivery? – How can we increase diversity?
  • combinatorial synthesis of lipid-like materials
  • RNA Interference – RNA Tx for Liver: Transthyretin-(TTR)
  • TTR in primates, in Humans – Delivery of sRNAi – new class of machines
  • Chylomicron metabolism: The rate of dietary : Mechanism of APoE mediated iLNP delivery
  • sRNAi are not limited for hypatocytes
  • One injection – 5 genes silencing in lung endothelial cells
  • IMMUNE CELLS AS A TARGET FOR siRNA
  • Repaired liver cells in mouth: repopulation of the liver
  • How do we deliver Cas9 in vivo?
  • Modular Pharmacology: Deliver mRNA to inside cells? using nanoparticles
  • chemistry of nanoparticles will delivery to lungs not to liver or to liver not to lungs
  • inhaled nanoparticles for mRNA delivery
  • Cas9 – for gene editing – – Inject AAV-Virus — >> AAV +Cas( mRNA
  • Chemical modification for siRNA: guiding siRNA delivery
  • Guide RNA improve Genome editing
  • Full modification abolishes the function of sgRNA: Cas9-sgRNA
  • e-sgRNA – edited
  • PCKS9- hyperlipidemia — Nanoparticle for in vivo  Genome Editing
  • RNA NANOTHERAPEUTICS AND CANCER
  • Delivery to Immune system – Genome editing in vivo of CAR-Ts

Speakers:

·       Daniel Anderson,

·       Mark Davis,

·       Suzie Pun

 

12:30 PM – 2:00 PM Lunch Break

 

2:00 PM – 3:00 PM Panel ‘Translation of Nanomedicine to Patients’

Noubar Afeyan, John Maraganore, Bob Langer, Paula Hammond, Michelle Bradbury, Cristianne Rijcken

Moderated by Rebecca Spalding

Noubar Afeyan,

John Maraganore,

Bob Langer,

Paula Hammond,

Michelle Bradbury,

Cristianne Rijcken

 

 

3:00 PM – 4:30 PM Session III: Nanosystems and Devices

Sangeeta Bhatia Activity-based biomarkers for non invasive Cancer Detection, Classification and Monitoring

    • Biomarker paradigm for clinical decisions – Endogenous, singular, blood
    • Synthetic Biomarker paradigm for clinical decisions – Exogenous, multiples, urine
    • Endoprotease in Cancer: MMP9, MMP4
    • Synthetic Biomarkers: Sensitivity
    • Enzyme-responsive nanosensors and PK switch [acitvation fluorescence]
    • Benchmarking synthetic biomarkers against a blood biomarker: Urinary synthetic biomarkers outperform CEA
    • multi-compartment modeling for predicting PK
    • Enhancing sensitivity by nanosensor engineering for ovarian cancer detection
    • Mass barcodes enable multiplexing
    • Mass encoded synthetic biomarkers
    • Differentiating similar diseases with protease activity
    • Paper-based microfluidics in urine biomarker
    • synthetic breath biomarkers for lung disease
    • Protease-Responsive Imaging Sensor for Metastasis (PRISM) – localization of Tumor
    • In vivo Enzyme Profiling by Syntahtic Biomarkers

Rashid Bashir Micro and Nanotechnologies for Analysis of Tissues and Molecules

  • liquid biopsy, molecualar analysis of the tumor
  • spatial map of nuclei acids in tissue – Intra tumor heterogeniety
  • subclonal genetic diversity is important
  • laser capture microdissection
  • fluoresence in situ hybridization
  • Cryo-section on microwell array, pixelate and fix tissue inside wells amplification reagents loaded on chip – amplification reaction: Advantages over PCR
  • procees flow on chip
  • On-chip RT-LAMP: Spatial fluorescence analysis
  • ON CHIP RT-LAMP CONTROL: CANCER (red) VS NON-CANCER (blue)  – FTIR control same section
  • Single cell spatial RNA Seq
  • Hematology Analyzer – complete blood celll count  vs FLow cytometry
  • Cells and Proteins from a Drop of Blood

Convergence : The Future of Health – Cancer Center at Illinois

    • Medical Schools MUST Change  CurrentCurriculum vs Future Curriculum
    • NOW: Yr 1: Basic Science Yr 2: Basic Science Yr 3: Clinical Science +  Required rotation Yr 4: Clinical Science +  elective rotation
    • FUTURE: ALL GENOMICS +BIOENGINEERING to be integrated

Jim Heath A Molecular View of Immuno-Oncology, Institute of System Biology

  • Analytical Chemistry challenge:
  • Fundamental Immunology
  • Challenge CRISPR knocking out genes not for knocking in genes
  • Mutated proteins and NEO antiagens: mostly a computational task

Speakers:

·       Rashid Bashir,

·       Sangeeta Bhatia,

·       James Heath

  • Personalized Immuno-Oncology

 

4:30 PM – 4:50 PM Vladimir Bulović: MIT.nano Nanoscale Discoveries for Transformative Breakthroughs

Speakers:

·       Vladimir Bulović

    • MIT.nano
    • color depend on the size of the molecule
    • Drugs & Vitamins are nano-sized:
    • Scents are nano-sized – a fraction of an atom – ethylene – plant hormone – Pheromones – are nanosized
    • Nanoscale will define many future discoveries
    • 51% of the recently tenured SOS faculty – use nano
    • 67% of the recently tenured SOE faculty with benefits – use nano

 

4:50 PM – 5:00 PM Closing Remarks

Speakers:

·       Sangeeta Bhatia

 

Speakers


Daniel Anderson

Nanoparticle Formulations for RNA Therapy and Gene Editing

Daniel Anderson, PhD
Samuel A. Goldblith Professor of Applied Biology, MIT
Associate Professor, Chemical Engineering and Institute for Medical Engineering and Science, MIT
Member, Koch Institute, MIT

Rashid Bashir

Micro and Nanotechnologies for Analysis of Tissues and Molecules

Rashid Bashir, PhD
Executive Associate Dean and Chief Diversity Officer, Carle Illinois College of Medicine
Grainger Distinguished Chair in Engineering, Professor of Bioengineering, Electrical and Computer Engineering, Mechanical Science and Engineering, Materials Science and Engineering, and Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign

Angela Belcher

New Approaches for Finding Tiny Tumors: Towards Early Detection and Treatment of Ovarian Cancer

Angela Belcher, PhD
James Mason Crafts Professor and Professor of Biological Engineering, MIT
Member, Koch Institute, MIT

Sangeeta Bhatia

Protease Nanosensors for Cancer Detection, Classification and Monitoring

Sangeeta Bhatia, MD, PhD
Director, Marble Center for Cancer Nanomedicine
John J. and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science, MIT
Member, Koch Institute, MIT
Investigator, Howard Hughes Medical Institute

Vladimir Bulović, PhD

Nanoscale Discoveries for Transformative Breakthroughs

Vladimir Bulović, PhD
Director, MIT.nano
Associate Dean for Innovation, MIT School of Engineering
Fariborz Maseeh (1990) Professor of Emerging Technology, Department of Electrical Engineering and Computer Science (EECS), MIT

Mark E. Davis, PhD

Designing Nanoparticles to Safely Cross the Blood-Brain Barrier for Treating Brain Cancers

Mark E. DavisPhD  
Warren and Katharine Schlinger Professor of Chemical Engineering, California Institute of Technology
Member of the City of Hope Comprehensive Cancer Center
Member of the UCLA Jonsson Comprehensive Cancer Center

Sanjiv Sam Gambhir, MD, PhD

Bubble Based Nanodiagnostics

Sanjiv Sam GambhirMD, PhD  
Virginia and D.K. Ludwig Professor for Clinical Investigation in Cancer Research, Professor of Bioengineering, Professor of Materials Science and Engineering, Stanford University

James R. Heath

A Molecular View of Immuno-Oncology

James R. Heath, PhD
President and Professor, Institute for Systems Biology
Professor of Molecular and Medical Pharmacology, UCLA

Suzie H. Pun

Modulating Tumor-Associated Macrophage

Suzie H. Pun, PhD
Robert F. Rushmer Professor of Bioengineering, Adjunct Professor of Chemical Engineering, University of Washington

Ralph Weissleder

Developing Next Generation Diagnostics for Cancer

Ralph Weissleder, MD, PhD
Thrall Professor of Radiology and Professor of Systems Biology, Harvard Medical School
Director of the Center for Systems Biology at Massachusetts General Hospital

 

Panelists: Translation of Nanomedicine to Patients


Noubar Afeyan

Noubar Afeyan, PhD
Founder and CEO, Flagship Pioneering

Michelle S. Bradbury, MD, PhD

Michelle S. Bradbury, MD, PhD
Co-Director, MSK-Cornell Center for Translation of Cancer Nanomedicines & Director, Intraoperative Imaging Program
Member, Molecular Pharmacology Program, Sloan Kettering Institute
Attending, Radiology, Memorial Sloan Kettering Cancer Center
Professor, Gerstner Sloan Kettering Graduate School of Biomedical Sciences & Weill Medical College of Cornell University

Paula Hammond

Paula Hammond, PhD
Head, Department of Chemical Engineering, MIT
David H. Koch Professor of Engineering, MIT
Member, Koch Institute, MIT

Robert Langer

Robert Langer, ScD
David H. Koch Institute Professor
Member, Koch Institute, MIT

John Maraganore

John Maraganore, PhD
CEO and Director, Alnylam

Cristianne Rijcken

Cristianne Rijcken, PhD
Founder and Chief Scientific Officer, Cristal Therapeutics

Rebecca Spalding

Moderator

Rebecca Spalding
Biotech Reporter, Bloomberg

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Ferritin Cage Enzyme Encapsulation as a New Platform for Nanotechnology

 Reporter: Irina Robu, PhD

In bionanotechnology, biological systems such as viruses, protein complexes, lipid vesicles and artificial cells, are being developed for applications in medicine and materials science.  However, the paper published by Stephan Tetter and Donald Hilvert in Angewandte Chemie International Edition show that it is possible to encapsulate proteins such as ferritin by manipulating electrostatic interactions with the negatively charged interior of the cage.The primary role of ferritin is to protect cells from the damage caused by the Fenton reaction; where, in oxidizing conditions, free Fe(II) produces harmful reactive oxygen species that can damage the cellular machinery.

The ferritin family proteins are protein nanocages that evolved to safely store iron in an oxidizing world. Since ferritin family proteins are able to mineralize and store metal ions, they have been the focus of much research for the production of metal nanoparticles and as prototypes for semiconductor production. The ferritin cage itself is highly symmetrical, and is made up of 24 subunits arranged in an octahedral symmetry. Ferritins are smaller than other protein used for protein   encapsulation.   Their  outer  diameter is only 12 nm, whereas engineered lumazine synthase variants form cages with diameters ranging from about 20 to 60 nm.The ferritin cage displays remarkable thermal and chemical stability it is likely to modify the surface of the ferritin cage through the addition of peptide and protein tags. These characteristics have made ferritins attractive vectors for the delivery of drug molecules and as scaffolds for vaccine design.

In summary, the paper published in Angewandte Chemie International Edition is the first example of protein incorporation by a ferritin.  Dr. Donald Hilvert and colleagues have shown that AfFtn not only complexes positively charged guest proteins within its naturally negatively charged luminal cavity, but that the in vitro mixing technique can be extended to the encapsulation and protection of other functional  fusion proteins.

Hence, the recent discovery of encapsulated ferritins has identified an exciting new platform for use in bio nanotechnology. The use of synthetic biology tools will allow their rapid implementation in materials science, bio-nanotechnology and medical applications.

SOURCE

https://www.readbyqxmd.com/read/28902449/enzyme-encapsulation-by-a-ferritin-cage

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Novel Blood Substitute – ErythroMer

Reporter: Irina Robu, PhD

For years, scientists have tried ineffectively to create an artificial molecule that emulates the oxygen-carrying function of human red blood cell but the efforts failed because of oxygen delivery and safety issues. Now, a group of researchers led by Dr. Alan Doctor at Washington University in Saint Louis, are trying to resuscitate blood substitutes with a new nanotechnology-based, artificial red blood cell may overcome the problems that killed products designed by a team of companies such as BiopureAlliance PharmaceuticalsNorthfield Labs and even Baxter. Dr. Alan Doctor’s new company, Kalocyte is advancing the development of the

The donut-shaped artificial cells, ErythroMer are one-fiftieth the size of human red blood cells. ErythroMer is a novel blood substitute composed of a patented nanobialys nanoparticle. A special lining and control system tied to changes in blood Ph allows Erythromer to grab onto oxygen in the lungs and then dispense the oxygen in tissues where it is needed. The new artificial cells are intended to sidestep problems with vasoconstriction or narrowing of blood vessels.

Erythromer is stored freeze dried and reconstituted with water when needed but it can also be stored at room temperature which makes it for military and civilian trauma.

Trials have been successful in rats, mice, and rabbits, and human trials are planned. However, moving Erythromer into human clinical trials is still 8-10 years away.

SOURCE

https://www.thestreet.com/story/13913099/1/human-blood-substitutes-once-dead-now-resuscitated.html

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