Expression plasmid pJH114 containing the five E. coli bamABCDE genes which were under the control of a trc promoter, and with an octa-histidine (8 × His) tag at the C terminus of bamE was initially used for overexpression of BamABCDE complex in E. coli HDB150 cells16. Expression of the native BamABCDE complex was induced with 100 μmol l−1 isopropyl-β-D-1-thiogalactopyranoside (IPTG; Formedium) at 20 °C overnight when the absorbance of the cell culture at 600 nm reached 0.5–0.8. The selenomethionine-labelled BAM complexes were expressed in M9 medium supplemented with selenomethionine Medium Nutrient Mix (Molecular Dimensions) and 100 mg l−1 L-(+)-selenomethionine (Generon) using the similar conditions as the native BamABCDE. Both native and selenomethionine-labelled BamABCDE complexes were purified using a similar protocol. In brief, the cells were pelleted and resuspended in lysis buffer containing 20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 10 μg ml−1 DNase I and 100 μg ml−1 lysozyme and lysed by passing through a cell disruptor (Constant Systems) at 206 MPa. The lysate was centrifuged to remove the cell debris and unbroken cells, and the supernatant was ultracentrifuged to pellet the membranes at 100,000g for 1 h. The cell membranes were resuspended in solubilization buffer containing 20 mM Tris-HCl, pH 8.0, 300 mM NaCl, 10 mM imidazole and 1–2% n-dodecyl-β-D-maltopyranoside (DDM; all detergents were purchased from Anatrace) and rocked for 1 h at room temperature or overnight at 4 °C. The suspension was ultracentrifuged and the supernatant was applied to a 5-ml pre-equilibrated HisTrap HP column (GE Healthcare). The column was washed with wash buffer containing 20 mM Tris-HCl, pH 8.0, 300 mM NaCl and 35 mM imidazole and eluted with elution buffer containing 300 mM imidazole. The eluent was applied to HiLoad 16/600 Superdex 200 prep grade column (GE healthcare) pre-equilibrated with gel filtration buffer containing 20 mM Tris-HCl, pH 7.8, 300 mM NaCl and detergents. Different detergents were used in protein purification procedures. The purified BamABCDE complex was analysed by SDS–PAGE (Extended Data Fig. 1 and Supplementary Fig. 1), which indicated that BamB is not enough in the complex, and BamB is absent in the determined structure. We therefore decided to generate a new plasmid to express the BamABCDE complex. Additional copy of the E. coli bamB gene was introduced into pJH114 (ref. 16) after the 8 × His tag to generate a new expression plasmid pYG120 using a modified sequence and ligation-independent cloning (SLIC) method42. In brief, vector backbone and bamB gene fragments were amplified by PCR using Q5 Hot Start High-Fidelity DNA Polymerase (New England BioLabs), and plasmid pJH114 as template and primers PF_pJH114_SLIC (5′-GTTAATCGACCTGCAGGCATGCAAG-3′) and PR_pJH114_SLIC (5′-CTCTAGAGGATCTTAGTGGTGATGATGGTG-3′), and PF_EBB_SLIC (5′-TCATCACCACTAAGATCCTCTAGAGAGGGACCCGATGCAATTGC-3′) and PR_EBB_SLIC (5′-CTTGCATGCCTGCAGGTCGATTAACGTGTAATAGAGTACACGGTTCC-3′), respectively. Gel-extracted fragments were digested by T4 DNA polymerase (Fermentas) at 22 °C for 35 min followed by 70 °C for 10 min, and then placed on ice immediately. The digested fragments were annealed in an annealing buffer (10 mM Tris, pH 8.0, 100 mM NaCl and 1 mM EDTA) by incubating at 75 °C for 10 min and decreasing by 0.1 °C every 8 s to 20 °C. The mixture was transformed into E. coli DH5α for plasmid preparation. The DNA sequences were confirmed by sequencing. For the purification of the BamABCDE complex from the pYG120 construct, the wash buffer, elution buffer and gel filtration buffer were supplemented with different detergent combinations. A second gel filtration was performed to change detergents with gel filtration buffer containing 1 CMC N-octyl-β-D-glucopyranoside (OG) and 1 CMC N-dodecyl-N,N-dimethylamine-N-oxide (LDAO). For BamABCDE complex purification from construct pJH114, the wash buffer, elution buffer and gel filtration buffer were supplemented with 2 CMC N-nonyl-β-D-glucoside (β-NG) and 1 CMC tetraethylene glycol monooctyl ether (C8E4). The peak fraction was pooled and concentrated using Vivaspin 20 centrifugal concentrator (Sartorius, molecular mass cut off: 100 kDa). The selenomethionine-labelled proteins were purified in the same way as the native proteins of BamABCDE complex. The purified proteins were concentrated to 8–12 mg ml−1 for crystallization. For NaI co-crystallization, NaCl was replaced by NaI in the gel filtration buffer. All crystallizations were carried out by sitting-drop vapour diffusion method in the MRC 96-well crystallization plates (Molecular Dimensions) at 22 °C. The protein solution was mixed in a 1:1 ratio with the reservoir solution using the Gryphon crystallization robot (Art Robbins Instruments). The best NaI co-crystallized crystals were grown from 150 mM HEPES, pH 7.5, 30% PEG6000 and CYMAL-4 in MemAdvantage (Molecular Dimensions) as additive. The best native crystals were grown from 150 mM HEPES, pH 7.5 and 27.5% PEG6000. The best selenomethionine-labelled crystals were grown from 100 mM Tris, pH 8.0, 200 mM MgCl . 6H O, 24% PEG1000 MME and OGNG in MemAdvantage as additive. The crystals were harvested, flash-cooled and stored in liquid nitrogen for data collection. The data sets of selenomethionine labelled BAM complex were collected on the I03 beamline at Diamond Light Resources (DLS) at a wavelength of 0.9795 Å. All data were indexed, integrated and scaled using XDS43. The crystals belong to space group of P4 2 2, with the cell dimensions a = b = 254.16 Å, c = 179.22, α = β = γ = 90°. There are two complexes in the asymmetric unit. The structure was determined to 3.9 Å resolution (Extended Data Table 1) using ShelxD44, 45. Fifty-six selenium sites were found, which gave a figure of merit (FOM) of 0.32. After density modification using DM46, the BamACDE complex was clearly visible in the electron density map, but without BamB. Using the individual high-resolution models, the BamACDE complex was built using Coot47 by skeletonizing the electron density map and docking the BAM subunits in the electron density map with selenomethionine sites used as guides. Rigid body refinement was performed following manual docking. NCS refinement was used along with TLS refinement against groups automatically determined using PHENIX48. Restrained refinement was performed with group B-factors alongside reference model secondary structure restraints from higher resolution models. Weights were automatically optimised by PHENIX48. To obtain the BamABCDE complex structure, the new construct was used to produce sufficient BamB to form the BamABCDE complex. The data sets of BamABCDE complex were collected on the I02 beamline at DLS. The crystals belong to space group P4 2 2, with the cell dimensions a = b = 116.69 Å, c = 435.19 Å, α = β = γ = 90°. There is one complex molecule in the asymmetric unit. Although the crystals diffracted to 2.90 Å, the crystal structure of BamABCDE could not be determined by molecular replacement. BamABCDE complex was crystallized in presence of 0.2 M sodium iodide, and SAD data sets were collected at a wavelength of 1.8233 Å. Four 360° data sets were collected on different regions of the same crystal of NaI co-crystallization then combined. The phases were determined by ShelxD44, 45 at 4 Å resolution. Eleven iodide sites were found, which gave a FOM of 0.28. The phases were extended to 2.90 Å by DM46, and the model was built using Coot47 by skeletonizing the electron density map and docking the individual high-resolution subunits in the electron density map and rigid body fit this model into the higher resolution native data set while retaining and extending the free R set from the iodide data set. The BamABCDE complex was refined using PHENIX48. TLS groups were automatically determined using PHENIX48 and used for refinement along with individual B-factors. Weights were automatically optimised and secondary structure restraints were used. An E. coli bamA expression plasmid was constructed for functional assays using SLIC method as described above. An N-terminal 10 × His tag fused with bamA starting from residue 22 was amplified by PCR using Q5 Hot Start High-Fidelity DNA Polymerase (New England BioLabs), and plasmid pJH114 as template and primers PF_bamA_SLIC (5′-CCATCATCATCATCATCATCATCATGAAGGGTTCGTAGTGAAAGATATTCATTTCGAAG-3′) and PR_bamA_SLIC (5′-AGACTCGAGTTACCAGGTTTTACCGATGTTAAACTGGAAC-3′). Vector backbone was amplified from a modified pRSFDuet-1 vector (Novagen, Merck Millipore) containing an N-terminal pelB signal peptide coding sequence with primers PF_RSFM_SLIC (5′-CGGTAAAACCTGGTAACTCGAGTCTGGTAAAGAAACCGCTGC-3′) and PR_RSFM_SLIC (5′-ATGATGATGATGATGATGATGATGGTGATGGGCCATCGCCGGCTG-3′). Plasmids were prepared using GeneJET Plasmid Miniprep Kit (Thermo Scientific). Site-directed mutagenesis was performed according to a previously described protocol49 with slight modification (PCR conditions and the sequences of the primers are available on request). The sequences of the wild type and all mutant constructs of BamA were confirmed by sequencing. E. coli JCM166 cells3 transformed with the wild-type BamA or its mutants were plated on LB agar plates supplemented with 50 μg ml−1 kanamycin and 100 μg ml−1 carbenicillin in the presence or absence of 0.05% L-(+)-arabinose and grown overnight at 37 °C. Single colonies grown on arabinose-containing plates were inoculated in 10 ml LB medium supplemented with 50 μg ml−1 kanamycin, 100 μg ml−1 carbenicillin and 0.025% L-(+)-arabinose, and incubated at 200 r.p.m. at 37 °C for 16 h. For plate assays, the cells were pelleted and resuspended in fresh LB medium supplemented with 50 μg ml−1 kanamycin and 100 μg ml−1 carbenicillin, and diluted to an A of ~0.3 and streaked onto LB agar plates supplemented with 50 μg ml−1 kanamycin, 100 μg ml−1 carbenicillin in the presence or absence of 0.05% L-(+)-arabinose and cultured at 37 °C for 12–14 h. Western blotting was performed to examine protein expression levels of BamA in the membrane. 50 ml of overnight cultures of transformed JCM166 cells with respective wild-type or each mutant of BamA were pelleted. The cells were resuspended in 25 ml 20 mM Tris-HCl, pH 8.0, 150 mM NaCl and sonicated. The cell debris and unbroken cells were removed by centrifugation at 7,000g for 30 min. The supernatant was centrifuged at 100,000g for 60 min and the membrane fraction was collected. The membrane fraction was suspended in 5 ml buffer containing 20 mM Tris-HCl, pH 8.0, 150 mM NaCl and 1% 3-(N,N-dimethylmyristylammonio)-propanesulfonate (Sigma) and solubilized for 30 min at room temperature. Samples were mixed with 5 × SDS–PAGE loading buffer, heated for 5 min at 90 °C, cooled for 2 min on ice and centrifuged. Ten microlitres of each sample was loaded onto 4–20% Mini-PROTEAN TGX Gel (Bio-Rad) for SDS–PAGE and then subjected to immunoblot analysis. The proteins were transferred to PVDF membrane using Trans-Blot Turbo Transfer Starter System (Bio-Rad) according to the manufacturer’s instructions. The PVDF membranes were blocked in 10 ml protein-free T20 (TBS) blocking buffer (Fisher) overnight at 4 °C. The membranes were incubated with 10 mL His-Tag monoclonal antibody (diluted, 1:1,000) (Millipore) for 1 h at room temperature followed by washed with PBST four times and incubated with IRDye 800CW goat anti-mouse IgG (diluted, 1:5,000) (LI-COR) for 1 h. The membrane was washed with PBST four times and PBS twice. Images were acquired using LI-COR Odyssey (LI-COR). The JCM166 cells containing the double cysteine mutants Gly393Cys/Gly584Cys, Glu435Cys/Ser665Cys and Glu435Cys/Ser658Cys of BamA were cultured overnight in LB medium with 50 μg ml−1 kanamycin, 100 μg ml−1 carbenicillin and 0.025% L-(+)-arabinose, respectively. The membrane fraction from 50 ml cells was isolated and solubilized as described above. The samples were mixed with SDS loading buffer and then boiled for 5 min or kept at room temperature for 5–10 min. SDS–PAGE was performed at 4 °C by running the gel for 60 min at 150 V. The proteins were transferred to PVDF membrane as described above and the BamA mutants were detected by western blotting. All molecular dynamics simulations were performed using GROMACS v5.0.2 (ref. 50). The Martini 2.2 force field51 was used to run an initial 1 μs Coarse Grained (CG) molecular dynamics simulation to permit the assembly and equilibration of a 1-palmitoly, 2-cis-vaccenyl, phosphatidylglycerol (PVPG): 1-palmitoly, 2-cis-vaccenyl, phosphatidylethanolamine (PVPE) bilayers around the BamABCDE complexes52. Using the self-assembled system as a guide the coordinates of the BAM complexes were inserted into an asymmetric model E. coli OM, comprised of PVPE, PVPG, cardiolipin in the periplasmic leaflet and the inner core of Rd1 LPS lipids in the outer leaflet53, using Alchembed54. This equated to a total system size of ~500,000 atoms. The systems were then equilibrated for 1 ns with the protein restrained before 100 ns of unrestrained atomistic molecular dynamics using the Gromos53a6 force field55. The lipid-modified cysteine parameters were created from lipid parameters for diacylglycerol and palmitoyl and appended to the parameters of the N-terminal cysteines56. Systems were neutralised with Mg2+ ions, to preserve the integrity of the outer leaflet of the OM, and a 150 mM concentration of NaCl. All ~500,000 atom systems were all run for 100 ns, with box dimensions in the region of 200 × 200 × 150 Å3. To assess the stability of the subunit stoichiometry we assessed various combinations of BAM assemblies. For both BamACDE and BamABCDE crystal structures, we investigated ABCDE, AD and A alone, with three repeats each; while single simulations were also performed for BamABD, ACD, ADE, ABDE and ACDE, with a total simulation time equating to 2.8 μs. In cases where domains or subunits were missing these were added to the complex by structurally aligning the resolved units from the companion structure. For BamB, this was added to the BamACDE complex by structurally aligning POTRA 3. For the full BamC, this was added to the BamABCDE by aligning the resolved N-terminal domains. Individual protein complexes were configured and built using Modeller57 and PyMOL (The PyMOL Molecular Graphics System, version 1.8, Schrödinger, LLC). All simulations were performed at 37 °C, with protein, lipids and solvent separately coupled to an external bath, using the velocity-rescale thermostat58. Pressure was maintained at 1 bar, with a semi-isotropic compressibility of 4 × 10−5 using the Parinello–Rahman barostat59. All bonds were constrained with the LINCS algorithm60, 61. Electrostatics was measured using the Particle Mesh Ewald (PME) method62, while a cut-off was used for Lennard–Jones parameters, with a Verlet cut-off scheme to permit GPU calculation of non-bonded contacts. Simulations were performed with an integration time-step of 2 fs. The linear interpolation between the three structures was performed using the morph operation in Gromacs tools50. Analysis of the molecular simulations was performed using Gromacs tools50, MDAnalysis63 and locally written scripts. Conservation analysis was performed using Consurf64. For each subunit, 150 homologues were collected from UNIREF9065 using three iterations of CSI-Blast66, with an E-value of 0.0001. The Consurf scores were then mapped into the B-factor column for each of the subunits.
Boy M.,University of Helsinki |
Sogachev A.,Technical University of Denmark |
Lauros J.,University of Helsinki |
Zhou L.,University of Helsinki |
And 2 more authors.
Atmospheric Chemistry and Physics | Year: 2011
Chemistry in the atmospheric boundary layer (ABL) is controlled by complex processes of surface fluxes, flow, turbulent transport, and chemical reactions. We present a new model SOSA (model to simulate the concentration of organic vapours and sulphuric acid) and attempt to reconstruct the emissions, transport and chemistry in the ABL in and above a vegetation canopy using tower measurements from the SMEAR II at Hyytïal̈a, Finland and available soundings data from neighbouring meteorological stations. Using the sounding data for upper boundary condition and nudging the model to tower measurements in the surface layer we were able to get a reasonable description of turbulence and other quantities through the ABL. As a first application of the model, we present vertical profiles of organic compounds and discuss their relation to newly formed particles. © 2011 Author(s).
Everyone wants to foresee the future, prompting the Industrial Research Institute to present their summary findings looking ahead in “IRI 2038: Envisioning the Future of R&D.” Among their findings, some of the key future trends are already being considered by thought leaders across a variety of industries; moreover, several related topics were specifically highlighted in a series of recent articles. Of particular relevance to many stakeholders is the increasing trend of “open” innovation models, where a variety of internal and external resources are leveraged to discover, develop and, ultimately, commercialize new products across industries. Taking into consideration the points and observations raised in the preceding list of articles, this article will provide some additional recommendations for industrial R&D stakeholders to consider, with particular emphasis on measurement or analytical sciences. Consider the various quality assurance requirements — across industries — where comprehensive molecular characterization of materials is required during the product life cycle. Typically, specifications used are abstracted from analytical test methods — whether to validate the fidelity of composition (in material discovery), to validate control during process development, or to support the release of materials in commercial markets. This process of abstraction culminates in the ability for quality assurance stakeholders to support data review and material release. Correspondingly, informatics systems are built to support these workflows with proven efficiency. However, for certain test methods, the reduction of data to numerical and textual descriptors limits additional use of the data acquired. As an example, consider impurity profiling in drug substances and drug products. By reducing the validated HPLC-UV-MS methods to a collection of tracked components (by retention time and area percent), it becomes difficult to leverage the rich information contained in chromatograms and corresponding spectra. Luckily, modern chromatography data systems (CDS), scientific data management systems (SDMS) and document management installations afford the retrieval of all associated analytical data, if one knows in advance what they’re seeking, with varying levels of effort. We have observed that, at times, the solution to a manufacturing challenge can be found in well-indexed impurity profiles only by investigating profiles in their full form. Extending this example: consider the scenario where a material/substance lot fails due to the presence of a new impurity. Determining the source of this new impurity is the first step in containment and abatement. Imagine being able to perform a “spectral search” across all native analytical data for a substance — from its initial raw materials, through process intermediates, to final composition and formulation. Moreover, upon discovery of a new, raw material-related impurity, there is value in assessing whether other substances in a firm’s commercial inventory are potentially at risk of future quality failures. Analysis and Data: Impact on architecture and system functionality The interrelatedness of certain analytical information is an additional consideration. In the example above, compositional profiling by HPLC-UV-MS may be captured by one analytical data file. However, many substance or product specifications require multiple techniques from different instruments, collected at different times, by different places and perhaps outside the company. (This is especially relevant when leveraging material generated and tested by external partners, as described by Fahie and Guggenheim. To support such interrelatedness, firms must be able to not only sufficiently index individual analytical data files, but must also afford “analysis assembly” capabilities to provide users with a comprehensive “story” for relevant analyses. Consider formulation profiling as an example. The following list of “related data” must be “assembled” to present a comprehensive assessment of a product formulation: From an architecture perspective, firms can decide to put the analysis assembly capability at the data mart tier, or store so-called Assembled Analysis Results if their model leverages relational data models at the information tier. Moreover, firms should recognize the need to implement comprehensive analysis ontologies or taxonomies — beyond analytical technique-based tagging. We propose that firms build data models that support users when they need to query and retrieve these complex, interrelated data. Finally, the ability to visualize these interrelated analysis project data in a simple, facile interface is critical to a variety of workflows, similar to Figure 1. We have observed that many of the innovation management systems are primarily concerned with project timeline/deliverable management; often neglecting how information between collaborators is governed. The articles referenced above describe some of the emerging data frameworks that will support firms’ operating models now and in the future, along with some specific workflows within a partner ecosystem. In addition to the ever-present concern for secure information exchange and sequestration, some additional considerations may be relevant for R&D stakeholders. Similar to the Analysis Assembly capability described above, complex, assembled data files may come from sources from a variety of partners, across the product lifecycle. Furthermore, systems must support a complex permissions hierarchy, which allows for a variety of partner relationship modalities. These can range from submission-only to open data access. Finally, certain pertinent partner relationship management attributes must also be associated with analytical data files. Consider also that, if these attributes are associated appropriately, specific key performance indicators for individual partners can be obtained, in addition to the existing contractual obligations assessed using existing systems. Some examples include response/fulfillment timing, activity tracking and subjective interaction assessment. The articles referenced above indicate the future trends predicted in the IRI 2038 findings are observable today. We assert that industrial externalization efforts and modern IT infrastructure investments are weak signals of the future being realized sooner than 2038. However, innovation stakeholders must recognize that certain functionality must be implemented within this new architecture to meet critical decision support requirements. Specifically, for analytical sciences, the aforementioned analysis assembly toolset must be implemented to assure business value. As R&D operating models continue to evolve, stakeholders must continue to make strategic investments that support corporate innovation velocity, productivity and risk mitigation. These investments must ultimately support facile collaboration, robust data management practices and IP protection. But, as these systems are implemented, stakeholders must be mindful that certain scientific data requires specialized ontologies or taxonomies — particularly in data resulting from measurement or analytical sciences. For such data to be effectively leveraged in business-critical decision-making, the appropriate functionality for analysis assembly must be carefully implemented. Andrew Anderson is Vice President, Business Development, and Graham A. McGibbon is a Manager, Scientific Solutions and Partnerships, at ACD/Labs. They may be reached at editor@ScientificComputing.com.
The articles “The Future-as-a-Service” and “Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World” nicely lay out the informatics challenges associated with dealing with LIVE data (raw) versus DEAD1 data (processed) when it comes to the externalization of scientific research and development. As their authors point out, there are a multitude of less-than-adequate solutions that can be applied to this problem. One of the biggest contributors to the problem is how each business or portion of a business enters into a relationship with an external CRO partner. Most, if not all of the time, the business driver is reactive, functional and financial, rather than strategic. For example, a small firm may have valuable IP but not the funding or time to set up their own in-house R&D laboratories, or large more well-established companies may be looking for ways to reduce costs and/or grow earnings. Neither of these situations creates environments in which a holistic approach to managing external R&D relationships are easily supported, nurtured and grown. Having a well-thought-out strategic sourcing model allows a company to fully integrate internal human and capital resources within the context and framework of effective utilization of external resources. This can lead to improved FTE hiring and capital acquisition decisions. An added benefit is that CRO partners, or potential partners, are more easily able to identify where they may fit into the innovator’s business model, allowing them to better focus their resources and investments to meet the innovator’s R&D goals. In this article, we describe a model in which R&D organizations develop a sourcing strategy that is aligned with how they add value to their customers and shareholders. Many businesses understand this at a high level, but have failed to translate it to their business operations. Within the scope of this document, we limit our discussion to analytical-based activities, but this model can be applied to all functions within an R&D organization and into manufacturing. There are significant benefits to an entire organization using a single model, as it ensures alignment between strategic priorities across departments. At the end of this article, we will describe how the application of our strategic sourcing model has enabled a mutually beneficial three-way partnership that enables LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO. We define “strategic” as having two categories: For material generation functions of a company, strategic definition number 1 usually dominates, whereas for analytical functions, strategic definition number 2 usually dominates. Too often, companies decide to outsource work that is considered “routine.” We avoid using this term, as by its very nature it leads people within the organization to consider work that is described as routine as less important and, therefore, less valuable, which by definition limits people’s willingness to invest in a sustainable solution to support it. We suggest that work that is considered less important or less valuable simply should not be done by either the innovator or their partners. Companies should limit their efforts to work that is both important and critical to the company’s efforts. Within the definition of “important” there are two sub-categories: By our definition, all urgent work is critical to the company’s strategic efforts, as the urgency implies that other functions are waiting for the data to make decisions. As a result, all important and urgent work should be done internally (Figure 1, Quadrant 1), if possible. When this is not possible, doing this work at a CRO but inside an FTE team is the next best option (Figure 1, Quadrant 2). This solution limits the number of people who are exposed to the work product and, further, the priorities of an FTE team can be quickly adjusted in concert with the needs of the material generation functions. In the “Important and Not Urgent” category there are two sub-categories: Predictable is defined as worked that is governed by a qualified or validated method and a protocol. The qualified or validated method ensures that the method will produce meaningful results in a reliable manner, and the protocol defines how much work there is and when it needs to be executed or completed. These two attributes make the work product predictable, but not necessarily easy (Figure 1, Quadrant 3). The last category of work we have defined as “niche” (Figure 1, Quadrant 4). In our model, “niche” work is usually very important, but seldom strategic, and is often governed or mandated by industry regulations. Examples of work in this category are extractable and leachable or trace metals analysis. In this category, the innovator is specifically leveraging the technical expertise of CROs. These niche disciplines may require specific skill sets for which an innovator only has occasional need, whereas a CRO can aggregate need from several innovators to support permanent staff. Once a company has developed good agreement of what are strategic, predictable and niche work activities, a thoughtful approach toward outsourcing can be applied. We have taken a four-quadrant model approach that categorizes work activities while accepting the reality that, most of the time, the 80/20 rule applies, and the lines/interfaces between categories/quadrants can become blurred. Having a well-articulated sourcing model allows a company to agree on a standardized language — agreeing that only important work should be performed whether it is strategic, predictable or niche. This improves predictability for all involved by setting clear expectations for work done internally, and clarifies the role of outsourcing. Defining exactly what work should be externalized also eliminates the need for “approval” for every outsourcing decision. This has the major advantage of enabling a company to accurately and openly articulate its sourcing strategy to CROs, allowing CROs to tailor their customer outreach for what business might be available and to remove focus from what will not be available. In our model, work activities above the horizontal line can be actively prioritized by the innovator, whereas below-the-horizontal-line work is scheduled and prioritized by the CRO within the context of the associated business agreements. This is why unit work activity below the line is less expensive than similar corresponding work above the line. Predictable, non-strategic work is ideal for outsourcing. In general CROs set up their business to work in the predictable space. This is necessary so they can properly bid on work proposals. As soon as the work becomes too unpredictable, it becomes better-suited for a CRO FTE team, or an in-house FTE effort. However, through the application of this model, companies can significantly influence and change the CRO business through active engagement, partnership, and training of the CRO staff. Our approach allows for innovators and CROs to explore the natural migration of strategic work from Quadrant 1 that was once strategic, counterclockwise through the FTE team (Quadrant 2) and, ultimately, into the predictable space (Quadrant 3), or clockwise from strategic work (Quadrant 1) into the niche space (Quadrant 4) and again, ultimately, into the predictable space (Quadrant 3). Recently, our industry has observed migrations from the strategic (Quadrant 1) in both clockwise and counterclockwise directions for metals analysis and extractable and leachable analysis. Ultimately, having a well-defined model that drives outsourcing decisions allows for mutually beneficial investments by both the innovator, as well as the CRO. Even the best strategic sourcing model is significantly hampered by the difficulty innovators have getting access to all the LIVE data that a CRO generates on their behalf. To this end, we have recently embarked on collaboration with key partners to invest in data handling solutions that will enable LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO. Enabling the immediate and seamless transfer of the LIVE raw data allows us to further reconsider the current boundaries for what work can be performed at a CRO. Automated transfer of data from the CRO network provides key advantages, the absence of which can lead to hesitancy towards adapting a sourcing model like the one described in Figure 1. The first advantage is the elimination of innovator FTE time spent delivering numerical results to sample generating groups. This decreases the turnaround time for delivery of results which may be used to make decisions, examine trending, and complete regulatory filings. Although companies may utilize different software to report these numbers, creating a translation and delivery mechanism as part of the automated data delivery process is generally a simple customization. Secondly, the automated transfer enables delivery of sample and instrument metadata. Any recorded information regarding the experiments completed can be categorized and delivered to the innovator’s systems. For example, metadata such as the instrument preventative maintenance status can be accessed by the innovator as if it had been collected in the innovators laboratory. A third key advantage is providing the innovator access to LIVE raw data. This provides the innovator with several important functionalities. It enables the innovator to dig deeper into the data to answer questions. For example, “have we seen this peak before?”, or in a pre-approval inspection a regulator could ask, “can you show us the raw data?” It also allows the presentation of data collected internally and externally to be consolidated. Overlays for a comparability reports can easily be constructed with data from internal analytical functions and CROs together. The innovator is able to subject CRO data to the same disaster recovery and redundancy standards that it would for internally collected data. Some innovators have engineered solutions to allow a CRO to deliver data remotely into their internal systems. This arrangement achieves similar endpoints; however, it may not be a sustainable solution. Granting access to innovator systems requires a significant investment in a specific CRO, and thus could impinge on a competitive environment to address CRO capacity or performance issues. Further, it requires innovator-specific training, software licenses, and IT support. Those costs are typically the burden of the CRO. Creating a sustainable, expandable CRO-innovator data automation process requires two major considerations. The first hurdle is to address the wide range of data types deriving from the laboratories of a CRO and an Innovator. Even for analytical data collected internally at most innovators, there is no universal and sustainable solution for viewing and processing current and historical data. For a single discipline of analytical methods, there are likely several instrument and software vendors, each providing a proprietary data format. Extending your network to analytical labs in the CRO network further increases the variety of instrument and software vendors used. These inconsistencies can be overcome by utilizing pioneering software solutions, such as the ACD/Spectrus Platform (Advanced Chemistry Development, Toronto), which can view, process and store data collected on instruments from most leading analytical vendors. Further, the Allotrope Foundation aims to create a universal data format to address this serious business need, already recruiting a significant coalition of industry leaders to drive change in analytical data standards. These types of solutions are able to accommodate the many data formats that may be delivered from the CRO network. Importantly, the movement of data between CRO and innovator systems must be automated with minimal FTE effort. This can be achieved via several technological means. Solutions are available from vendors, such as BioVia, that physically shepherd the data between locations. One major concern from CROs is the access that each innovator has to their internal systems, which contain data from all of their clients. To address this, an FTP (file transfer protocol) site can be used as a neutral intermediary, allowing the CRO to deliver exactly the files it wants to send (Figure 2). This solution also allows customization of the cadence of data delivery. For example, the data can be uploaded as soon as it is collected, processed or fully reviewed. On the other side of the solution, the innovator can automate the delivery, processing, and archival of the files using a simple periodic sweep of the FTP site. Once the data is housed internally, the delivery of relevant data to internal systems is completed using an automation routine. For CROs, where the data itself is their product, their business process stresses consistency and standardization in data reporting. This makes automated delivery of data, such as numerical endpoints to a LIMS system, a sustainable automation requiring only infrequent lifecycle management. Once file-naming and metadata standards are set for the first participating CRO, the requirements for data sharing and delivery can be built directly into the RFP for the work. By incorporating these considerations into a data-sharing solution, the access to data collected internally or at a CRO are truly on the same playing field. This equality enables innovators to judiciously apply the Analytical Sourcing model described above. A robust and well-articulated sourcing strategy coupled with a seamless way to transfer LIVE data between CROs and innovators allows innovators to define CROs in the context of “partners.” This paradigm enables conversations to focus more around what work should be performed internally and what work should be performed at the CRO. This paradigm can enable both the innovator and CRO to efficiently utilize resources to meet their long-term strategic goals. Brian Fahie is Director, Technical Development and Evan Guggenheim is Scientist II, Technical Development, at Biogen. They may be reached at editor@ScientificComputing.com.
No statistical methods were used to predetermine sample size. For calibrating the duration of the dark housing period before light exposure, C57Bl6 wild-type mice were housed in a standard light cycle until they were placed in constant darkness for varying amounts of time before analysis at postnatal day 56. At P56, all mice were either sacrificed in the dark (dark-housed condition) or light-exposed for 1, 3, or 7.5 h before being sacrificed. The eyes of all animals were enucleated (for the dark-housed condition, enucleation was performed in the dark) before dissection of the visual cortex in the light. For RiboTag-experiments, mice were reared in a standard light cycle and then housed in constant darkness for two weeks starting from P42; at P56, all mice were either sacrificed in the dark (dark-housed condition) or light-exposed for 1, 3, or 7.5 h before being sacrificed. Additional cohorts of mice for the ‘standard’ condition were housed in a standard light cycle until P56 when they were euthanized. The eyes of all animals were enucleated (for the dark-housed condition, enucleation was performed in the dark) before dissection of the visual cortex in the light. Total RNA was extracted with TRIzol reagent (Sigma) according to the manufacturer’s instructions, and RNA quality was assessed on a 2100 BioAnalyzer (Agilent); all RNAs were treated with DNaseI (Invitrogen) before reverse transcription. For the cloning of riboprobes, total RNA was extracted from whole adult C57Bl6 wild-type mouse brains and cDNA was prepared using SuperScript II kit (Life Technologies). For real-time quantitative PCR experiments aimed at calibrating the duration of the dark housing period, total RNA was extracted for each sample from the visual cortices of one animal. For real-time quantitative PCR experiments aimed at testing the efficacy of shRNA constructs directed against Igf1, total RNA was isolated from two pooled 24 wells of cultured cortical neurons for each condition. For qPCR experiments, RNA was reverse-transcribed with the High Capacity cDNA Reverse Transcription kit (Life Technologies). Real-time quantitative PCR reactions were performed on the LightCycler 480 system (Roche) with LightCycler 480 SYBR Green I Master. Reactions were run in duplicates, triplicates or quadruplicates, and β-actin (Actb) or β3-tubulin (Tubb3) levels were used as an endogenous control for normalization using the ΔΔC method24. Real-time PCR primers were designed using the Universal ProbeLibrary (Roche) as exon-spanning whenever possible and answered the following criteria: linear amplification over three orders of magnitude of target concentration, no amplification product in control samples that were not reverse-transcribed (that is, control for contamination with genomic DNA), no amplification product in control samples where no template was added (that is, control for primer dimers), amplification of one singular product as determined by melt-curve analysis and analysis of the product in agarose gel electrophoresis and sequencing of the PCR product. The qPCR primers used in this study are listed in Supplementary Table 6. For analysis of light-induced gene expression in wild-type mice, the gene expression levels were analysed in four mice (two males and two females) at each time point. The data were calculated as fold change relative to the average of the overnight dark-housed condition and normalized to the average of the maximally induced time point. Data in figures represent the mean and s.e.m. of four mice. For assessing Igf1 levels in cortical cultures infected with shRNA-expressing lentiviral constructs, qPCRs were performed in quadruplicates for each condition and fold changes were calculated relative to the non-infected non-stimulated cultures. Data were normalized to the maximally induced condition in each biological replicate, and data in figures represent the mean and s.e.m. of three biological replicates. Immunoprecipitation and purification of ribosome associated RNA was performed essentially as described6, 8, with minor modifications: lysis of the samples was performed in the presence 10 mM Ribonucleoside Vanadyl Complex (NEB, Ipswich, MA), and immunoprecipitation was performed with a different anti-HA antibody (HA-7, 12 μg per immunoprecipitation, Sigma). In brief, the visual cortices were dissected, flash frozen in liquid nitrogen and then kept at −80 °C until further processing. Visual cortices from three individual animals (each sample contained both male and female animals) were pooled for each biological replicate, and three biological replicates were performed. After lysis of the tissues and before immunopurification, a small fraction of lysate of each sample (that is, ‘input’) was set aside and total RNA was extracted with TRIzol reagent followed by the RNEasy Micro Kit’s procedure (Qiagen, Valencia, California). After immunopurification of the ribosome-associated RNAs, RNA quality was assessed on a 2100 BioAnalyzer (Agilent, Palo Alto, California) and RNA amounts were quantified using the Qubit 2.0 Fluorometer (Life Technologies). Only samples with RIN numbers above 8.0 were considered for analysis by qPCR and RNA-seq. For all RNA samples of sufficient integrity, 5–10 ng of RNA were SPIA-amplified with the Ovation RNA Amplification System V2 (NuGEN, San Carlos, California), yielding typically 5–8 μg of cDNA per sample. Quantitative RT–PCR was performed as described above and relative expression levels were determined in every experiment by normalizing the Ct-values to those of beta-Actin (ActB) from the 0 h input using the ΔΔC method24. To determine the fold-enrichment (IP/Input), the actin-normalized expression levels for every time point of every biological replicate were averaged, and the grand averages from the IP and Input were divided to find the IP/Input ratio. To calculate fold-induction for each biological replicate, each time point was divided by the maximal value occurring in that biological replicate, such that the maximal value was set to 1 in each biological replicate. The mean and standard error were calculated at each time point from these normalized values. All samples were analysed by qPCR for purity and light-induced gene expression before analysis by high throughput sequencing. SPIA-amplified samples from RiboTag-immunoprecipitated fractions for each of the five stimulus conditions and each of the five Cre lines were prepared as described above and processed in triplicate (75 samples total). For preparing sequencing libraries, 2 μg of each amplified cDNA were fragmented to a length of 200–400 bp using a Covaris S2 sonicator (Acoustic Wave Instruments) using the following parameters: duty cycle: 10%, intensity: 5, cycles per burst: 200, time: 60 s, total time: 5 min. After validating the fragment length of the sonicated cDNA using a 2100 BioAnalyzer (Agilent, Palo Alto, California), 2 μg of the fragmented cDNA were used for sequencing library preparation using the PrepX DNA kit on an Apollo 324 robot (IntegenX). The quality of completed sequencing libraries was assessed using a 2100 BioAnalyzer (Agilent, Palo Alto, California) and the completed libraries were sequenced on an Illumina HiSeq 2000 instrument, following the manufacturer’s standard protocols for single-end 50 bp sequencing with single index reads. Sequencing typically yielded 30–80 million usable non-strand-specific reads per IP sample. Reads were mapped to the mm9 genome using TopHat (v.2.0.13) and Bowtie (220.127.116.11)25. On average, ~70% of mapped IP reads were uniquely mapped to the mm9 genome allowing for 0 mismatches and were therefore assignable to genic features (one RiboTag-seq library (Sst-cre, standard-housing, biological replicate 2) was excluded from analysis due to low mappability). Values from all IP libraries were normalized using Cufform (v.2.2.1), and values from the Cuffnorm output file ‘genes-Count_Table’ (normalized reads) were taken as a proxy for gene expression. P values were generated for each Cre line for each dark–light conditions using Cuffdiff (v.2.2.1) using the time series (-T) flag based on three biological replicates. To identify transcripts regulated by visual experience, for each biological replicate of each Cre line, the fold change in normalized reads was calculated for each gene at every time point (dark-housed/standard-housed, 1 h light/dark-housed, 3 h light/dark-housed; 7.5 h light/dark-housed). Genes were flagged as experience-regulated in a given Cre line if they met the following conditions in at least one sample: (1) P value <0.005, (2) mean fold change of twofold or greater, (3) fold changes of 2 or higher in 2 of 3 biological replicates, (4) the mean expression value in at least one sample must be above absolute expression threshold (set at the 40th percentile of all observed values). To determine in which Cre lines genes were regulated by experience, genes were simply classified according to the above criteria. However, for this analysis we excluded the Gad2-cre line, since Pv-, Sst- and Vip-cre all label subsets of the neurons labelled by Gad2-cre. However, we did detect genes regulated solely in Gad2-cre, but no other Cre lines; we reasoned that these genes are probably regulated by experience in a population of 5HT3aR+/VIP− neurons that are contained in Gad2-cre but none of the other Cre lines. We classified the set of experience-regulated genes into categories ‘early’, ‘late’, and ‘long-term’ based on the fastest kinetics observed. When genes were found to be elevated and/or suppressed at multiple time points, we assigned them to the categories based on the most rapid observed change. For example, while Fos levels are elevated over dark housing at 1, 3 and 7.5 h of light exposure and suppressed after two weeks of dark housing, Fos is classified as ‘early-up’ because it is elevated at 1 h after light exposure. All linkage analysis was performed using the ‘single’ method and ‘Cityblock’ metric using Matlab’s linkage function. To determine the branch-order significance of the cladogram resulting from clustering of the 602 experience-regulated genes, we generated 1,000 cladograms from 602 sets of random expressed genes (including experience-regulated genes, with replacement) and asked how often we generated a cladogram with an identical branch order at the level of the Cre lines. Only 11 sets of 1,000 random genes sets generated an identical tree. For the purposes of this analysis, we only compared the branches above the level of the individual Cre line. To identify cell-type-enriched transcripts, an enrichment score was calculated for every transcript in every Cre line for each biological replicate. This enrichment score was calculated by dividing the maximum expression value observed in a given Cre line by the maximum expression value observed across all conditions for all other Cre lines (GABAergic subtypes were not required to be enriched above Gad2-cre). The enrichment scores for a set of known cell-type-specific genes were evaluated (Vglut1, Tbr1, Pvalb, Sst, Vip), and our threshold was set at the enrichment score of the cell-type-specific gene with the lowest score (Slc17a7/Vglut1, at 5.5-fold-enriched in Emx1-cre). Transcripts were considered to be expressed in a cell-type-specific manner (or ‘highly enriched’) in a given Cre line if their mean enrichment score was above this threshold and if the enrichment score exceeded this threshold in 2 out of 3 biological replicates. Cloning of all constructs was done using standard cloning techniques, and the integrity of all cloned constructs was validated by DNA sequencing. Templates for the riboprobes for Igf1, Gad1, Pvalb, Sst and Vip were prepared by PCR-amplification of cDNA fragments generated from total RNA isolated from adult C57Bl6 mouse brains (see Supplementary Table 7 for primer sequences) and cloning of the respective PCR fragments into the pBlueScript II vector (Agilent Technologies). Lentiviral shRNA constructs were generated by cloning shRNA stem loop sequences against Igf1 (Igf1 shRNA 1: GGTGGATGCTCTTCAGTTC; Igf1 shRNA 2: TGAGGAGACTGGAGATGTA) and Luciferase (Luc, control: ACTTACGCTGAGTACTTCG) into a modified version of pLentiLox3.726 in which the CMV promoter driving the expression of eGFP was replaced with an hUbc promoter and in which the loxP sites surrounding the hUbc-eGFP cassette were removed. The loop sequence used in these shRNA constructs is based on miR-25 (CCTCTCAACACTGG)27. shRNA-expressing AAV-constructs (pAAV-U6-shRNA-hUbc-Flex-eGFP) were made by first replacing the Flex-GFP-Gephyrin cassette in pAAV-Flex-GFP-Gephyrin22 with a Flex-eGFP cassette (resulting in pAAV-hUbc-Flex-eGFP) and then transferring the U6-shRNA cassettes from the pLentiLox constructs to pAAV-hUbc-Flex-eGFP. AAV constructs for the Cre-conditional co-expression of epitope-tagged IGF1.4 and eGFP or of eGFP alone were cloned by synthesizing the gBlocks (Integrated DNA Technologies) and using the gBlocks as templates for PCR amplification; the respective PCR products were then cloned into the pAAV-hUbc-Flex-eGFP (see above) by replacing the EGFP with the respective insert. This strategy yielded plasmids termed pAAV-hUbc-Flex-SSHA-IGF1.4-Myc-F2A-eGFP and pAAV-hUbc-Flex-F2A-eGFP, whereby the Cre-dependent inserts were driven by a human ubiquitin promoter (hUbc). The sequence for Igf1.4 was based on NM_001111275 (base pairs 277–752) and was modified in the following way: an HA epitope (TATCCtTATGATGTTCCAGATTATGCT) was inserted in frame between the Igf1.4 signal sequence and the beginning of the coding sequencing (cds) of Igf1.4, Igf1.4 was rendered resistant to the shRNA against Igf1 by introducing silent mutations into the target sequences specified above (sh1: TGTTGACGCGCTCCAATTT; sh2: TACGCCGGTTAGAAATGTA) and the followings tags were inserted in frame 3′ to the Igf1.4 coding sequencing: Myc epitope (GAACAAAAACTCATCTCAGAAGAGGATCTG), Furin cleavage site (CGGGCCAAGCGG) and a 2A peptide (GGCAGTGGAGAGGGCAGAGGAAGTCTTCTAACATGCGGTGACGTGGAGGAGAATCCCGGCCCT). The sequence for eGFP was based of the published sequence of eGFP. For pAAV-hUbc-Flex-F2A-eGFP a gBlock was synthesized containing the Furin cleavage site followed by the 2A site and eGFP. Detailed sequences are available upon request. For double-fluorescent in situ hybridization (FISH), wild-type C57Bl6 mice were dark-housed and light-exposed for 7.5 h as described above. After light exposure, the brains were dissected and fresh frozen in Tissue-Tek Cryo-OCT compound (Fisher Scientific) on dry ice and stored at −80 °C until use. FISH for Igf1 was essentially done as described28, 29: riboprobes were prepared by in vitro transcription of linearized plasmids containing the template of the respective probe. Riboprobes for Igf1 were labelled with UTP-11-Digoxigenin, while the riboprobes for the subtype markers (Gad1, Pvalb, Sst, Vip) were labelled with UTP-12-Fluorescein (Roche); all riboprobes were hydrolyzed to lengths of 200–400 bp after synthesis and validated for labelling with Dioxigenin or Fluorescein. For in situ hybridization, coronal sections (20 μm thick) of the visual cortex were cut on a cryostat and fixed in 4% paraformaldehyde for 10 min. Endogenous peroxidases were inactivated by treating the sections for 15 min in 0.3% H O in PBS, and acetylation was performed as described. Pre-hybridization was done overnight at room temperature, and hybridization was performed under stringent conditions at 71.5 °C. Following hybridization, stringency washes in SSC were performed as described at 65 °C. For immunological detection of the first probe (Igf1), the tissue was first treated with a blocking step for 1 h in blocking buffer (B2) at room temperature before the anti-Digoxigenin-POD antibody (Roche) was applied at a concentration of 1:1000 in blocking buffer for 1 h at room temperature. Following three washes in buffer B1 and an additional wash in buffer TNT (0.1 M Tris-HCl pH 7.5, 0.15 M NaCl, 0.05% Tween20), the Igf1 probe was detected by exposing the sections at room temperature in the dark for 20 min to TSA Plus Cy3 reagent (Perkin Elmer) diluted 1:100 in TSA working solution, after which the sections were washed three times in TNT buffer. Before the immunological detection of the second probe, the peroxidases for detecting the first probe were inactivated by treating the sections for 30 min with 3% H O , followed by three washes in PBS. After an additional blocking step in blocking buffer for 1 h at room temperature, the anti-fluorescein-POD antibody (Roche) was applied at a concentration of 1:1000 in blocking buffer overnight at 4 °C. Following three washes in buffer B1 and an additional wash in buffer TNT, the probes of the subtype markers were detected by exposing the sections at room temperature in the dark for 15 min to TSA Plus Cy5 reagent (Perkin Elmer) diluted 1:100 in TSA working solution, after which the sections were washed three times in TNT buffer. Finally, the sections were counterstained with DAPI (4′,6-diamidino-2-phenylindole, Molecular Probes) and mounted using Fluoromount-G (Southern Biotech). In each experiment, controls for hybridization specificity were included (sense probe for Igf1) as well as controls for ensuring the specificity of the immunological detection of the digoxigenin- and fluorescein-labelled riboprobes. FISH for Crh, Prok2 and Fbln2 was done using the RNAscope system (Advanced Cell Diagnostic); this was necessary since no reliable signal could be detected with the method described above for Igf1 FISH using DIG-labelled riboprobes. RNAscope probes for all genes were synthesized by ACD and all experiments were done according to the ACD’s protocol for fresh frozen brain sections. For quantifying of the expression pattern of all genes of interest (GOI, that is, Igf1, Crh and Prok2; Fbln2 could not be detected reliably), the visual cortices in each section were imaged on a Zeiss Axio Imager microscope with a 10× objective and 3 × 5 fields-of-view were ‘stitched’ into one compound image; in all cases, image exposures were kept constant throughout a given experiment for each channel. Compound images of each visual cortex were then imported to Photoshop, and additional layers were created for each probe (that is, one layer for the GOI and one layer for the subtype marker in each compound image). The cells positive for each probe were then marked with a dot in the new respective layer by two independent investigators in a blinded manner (one investigator marking GOI-positive cells and the other investigator marking subtype-marker-positive cells). Finally, the layers containing the dots of the identified positive cells were compiled into a separate image file together with the DAPI-layer and imported into ImageJ. In ImageJ, the images were analysed in a blinded manner by defining the visual cortex and its layers as regions of interest (ROI) based on the DAPI staining and quantifying the number of cells positive for either one or both markers per ROI. For each combination of probes (GOI together with each of the subtype markers), two visual cortices from four animals were analysed (a total of eight visual cortices for each combination). Concentrated lentiviral stocks were prepared and titrated essentially as described30. AAV stocks were prepared at the University of North Carolina (UNC) Vector Core and at the Children’s Hospital Boston Vector Core; see also Supplementary Table 8 for further details on AAV stocks. Primary cultures of cortical neurons were prepared from E16.5 mouse embryos as described6. In brief, 3 × 105 neurons per well were plated in 24-well dishes coated with poly-d-lysine (20 μg ml−1) and laminin (3.4 μg ml−1). Cultures were maintained in neurobasal medium supplemented with B27 (Invitrogen), 1 mM l-glutamine, and 100 U ml−1 penicillin/streptomycin, and one-third of the media in each well was replaced every other day. For testing of viral shRNA constructs, the cultures were infected at DIV 3 with concentrated viral stocks for 5 h at an MOI of 6. After infection, the cultures were washed twice in plain neurobasal medium after which the conditioned medium was returned to the dish and the cultures were continued to be maintained as described. At DIV 7, neuronal cultures were treated overnight with 1 μM TTX and 100 μM AP-5 to silence spontaneous activity before the cultures were depolarized at DIV 8 with 55 mM extracellular KCl as described6 and lysed in TRIzol after 6 h of stimulation. HEK293T cells were used for testing the expression and the biological activity of the epitope-tagged IGF1.4 constructs. HEK293T cells were cultured in DMEM (Life Sciences) containing 10% FCS and penicillin/streptomycin. Cells were transfected using lipofectamine (Life Technologies) and 18 h post transfection, the medium was replaced with DMEM containing 0.1% FCS; 42 h post transfection, the conditioned media were collected, spun down to remove cell debris and used immediately for stimulating non-transfected HEK293T that were serum starved for 3 days in DMEM containing 0.1% FCS. The conditioned media were applied to the serum starved cells for 15 min at 37 °C after which the cells were lysed in boiling SDS sample buffer and subjected to Western blot analysis essentially as described6, 31. For detecting the (phosphorylated) IGF1-receptor, the following antibodies were used: anti-IGF1-receptor-β (D23H3) XP Rabbit mAb (#9750, Cell Signaling, 1:1000) and anti-phospho-IGF1-receptor-β (Tyr1135/1136)/Insulin Receptor β (Tyr1150/1151) (19H7) Rabbit mAb (#3024, Cell Signaling, 1:1000). For determining serum IGF1 levels, we used the IGF1 Quantikine ELISA kit (R&D Systems), following the manufacturer’s instructions (P3 Vip-cre heterozygous pups were injected intracortically with the respective AAV and bled at P20). Mice were anaesthetized with 10% ketamine and 1% xylazine in PBS by intraperitoneal injection. When fully anaesthetized, the animals were transcardially perfused with ice-cold PBS for 5 minutes followed by 15 minutes of cold 4% PFA in PBS. Brains were dissected and post-fixed for one hour at 4 °C in 4% PFA, followed by three washes (each for 30 min) in cold PBS, and cryoprotection overnight in 20% sucrose in PBS at 4 °C. The following day, brains were placed in Tissue-Tek Cryo-OCT compound (Fisher Scientific), frozen on dry ice and stored at −80 °C. Coronal sections (20 μm thick) of the visual cortices were subsequently cut using a Leica CM1950 cryostat and used for subsequent experiments. For immunolabelling, the slides were blocked for 1 h with PBS containing 5% normal goat serum and 0.1% Triton X-100 (blocking solution). The samples were incubated overnight with different primary antibodies diluted in blocking solution, washed three times with PBS and then incubated for 45 min at room temperature with secondary antibodies and/or Hoechst stain (ThermoFisher Scientific). Slides were mounted in FluoromountG (Southern Biotech) and imaged on a Zeiss Axio Imager microscope. The following antibodies were used: mouse anti-HA (HA-7, Sigma; 1:1000), chicken anti-GFP (GFP-1020, Aves labs ; 1:1500), goat anti-mouse IgG (H+L) Alexa Fluor 488 (Highly Cross-Adsorbed, Life Technologies; 1:1,000), goat anti-chicken IgY (H+L) Alexa Fluor 488 (Life Technologies; 1:1,000). For analysing the brains of Igf1 Vip-cre WT and cKO mice, brains of three-week-old WT and cKO littermates were placed on the same slide to minimize variation. After cryosectioning, the slides were either counterstained immediately or stored at −20 °C before they were counterstained and imaged. Counterstaining was done with DAPI (4′,6-diamidino-2-phenylindole, Molecular Probes) in PBS for 15–30 min at room temperature, after which the sections were washed once in PBS and mounted in Fluoromount-G (SouthernBiotech). For cell counting experiments, coronal visual cortex sections were imaged using a Zeiss Axio Imager microscope with a 10× objective and typically, 3 × 5 fields-of-view were ‘stitched’ into one compound image. In all cases, image exposures were kept constant throughout a given experiment for each channel. Custom ImageJ and MATLAB macros were used to quantify the area of each cortical layer, the number tdTomato-positive cells per layer, and the size of tdTomato-postive cells. Briefly, regions of interest (ROI) encompassing the visual cortex and its layers were defined based on the DAPI counterstaining. While the width of these ROIs was kept constant throughout the analysis of all sections, the height of the ROIs was adjusted in each image according to the DAPI counterstaining in each section and the areas of each layer in each section were recorded. For analysing the number and soma size of tdTomato-postive cells in each layer, a threshold for each channel was determined based on multiple user-defined negative regions. Channels were thresholded and binarized, and a mask of each channel was created. The number of tdTomato- positive cells was determined by taking the logical AND of the DAPI and tdTomato channel masks and counting the number of components greater than 4 pixels in size in the double overlap of the masks of the two channels in each layer ROI. The soma size was calculated as the area of these double-overlapping components. Three animals per genotype and 4–6 visual cortex sections per animal were analysed, and these data were used to determine the mean and s.e.m. of the values reported for each genotype. All surgeries were performed according to protocols approved by the Harvard University Standing Committee on Animal Care and were in accordance with federal guidelines. Surgeries were performed on mice between P14 and P15. Animals were deeply anaesthetized by inhalation of isoflurane (initially 3–5% in O , maintained with 1–2%) and secured in the stereotaxic apparatus (Kopf). Animal temperature was maintained at 37 °C. The fur was shaved and scalp cleaned with betadine and 100% ethanol three times before an incision was made to expose the skull. Injections into the visual cortex were made by drilling a ~0.5 mm burr hole (approximately 2.7 mm lateral, 0.5 mm anterior to lambda) through the skull, inserting a glass pipette to a depth of 200–400 μm and injecting 250 nl of the respective AAV construct at a rate of 100 nl min−1. Five minutes post-injection, the glass pipette was retracted, the scalp sutured and the mouse returned to its home cage. All animals were monitored for at least one hour post-surgery and at 12 h intervals for the next 5 days. Post-operatively, analgesic (flunixin, 2.5 mg per kg) was administered at 12 h intervals for 72 h. For neonatal injections, pups post-natal day 3–5 were anaesthetized on ice for 2–3 min, and secured to a stage where their head was supported using a clay mould using standard lab tape. A bevelled glass pipette was lowered into visual cortex (approximately 2 mm lateral, 0.2 mm anterior to lambda), and 50 nl of the respective AAV virus was injected at a rate of 23 nl sec−1. Injections were made into eight sites (four on each hemisphere), and the mouse was then allowed to recover on a 37 °C warm plate before being returned to the home cage. For bilateral stereotaxic intra-cortical injections of AAV constructs for visual plasticity experiments, surgeries were performed on mice between P18 and P20. Animals were anaesthetized with isofluorane gas (1–2% in O ), and body temperature was maintained at around 37 °C with a heating pad during surgery. The head was held in place by standard mouse stereotaxic frame. The fur was shaved and scalp cleaned with betadine and 100% ethanol three times before an incision was made to expose the skull. Burr holes were drilled into the skull at the point of injection guided by stereotaxic coordinates and blood vessel patterns (approximately 2 mm and 2.7 mm lateral, 0.5 mm anterior to lamba) on both hemispheres. A 28-gauge Hamilton syringe (701RN) was inserted to a depth of 200–300 μm and 250 nl of the respective AAV construct was injected at the rate of 50 ml min−1. Five minutes post-injection, the Hamilton syringe was retracted, the scalp sutured and the mouse returned to its home cage. All animals were monitored for at least one hour post-surgery. Post-operatively, analgesic (meloxicam, 5–10 mg kg−1) was administered every 24 h for 2 days. Coronal sections (300 μm thick) containing the primary visual cortex were cut from P19-P21 mice using a Leica VT1000S vibratome in ice-cold choline dissection media (25 mM NaHCO , 1.25 mM NaH PO , 2.5 mM KCl, 7 mM MgCl , 25 mM glucose, 0.5 mM CaCl , 110 mM choline chloride, 11.6 mM ascorbic acid, 3.1 mM pyruvic acid). Slices were incubated in artificial cerebral spinal fluid (ACSF, contains 127 mM NaCl, 25 mM NaHCO , 1.25 mM NaH PO , 2.5 mM KCl, 2.5 mM CaCl , 1 mM MgCl , 25 mM glucose) at 32 °C for 30 min immediately after cutting, and subsequently at room temperature. All solutions were saturated with 95% O /5% CO , and slices were used within 6 h of preparation. Whole-cell voltage-clamp recordings were performed in ACSF at room temperature from neurons in primary visual cortex that were identified under fluorescent and DIC optics. Recording pipettes were pulled from borosilicate glass capillary tubing with filaments using a P-1000 micropipette puller (Sutter Instruments) and yielded tips of 2–5.5 MΩ resistance. All experiments were recorded with pipettes filled with 135 mM caesium methanesulfonate, 15 mM HEPES, 0.5 mM EGTA, 5 mM TEA-Cl, 1 mM MgCl , 0.16 mM CaCl , 2 mM Mg-ATP, 0.3 mM Na-GTP, 10 mM phosphocreatine (Tris), and 2 mM QX-314-Cl. Osmolarity and pH were adjusted to 310 mOsm and 7.3 with Millipore water and CsOH, respectively. Recordings were sampled at 20 kHz and filtered at 2 kHz. mEPSCs were isolated by holding neurons at −70 mV and exposing them to 0.5 μM tetrodotoxin, 50 μM picrotoxin and 25 μM cyclothiazide and were blocked by application of 25 μM NBQX and 50 μM CPP. mIPSCs were isolated by holding neurons at 0 mV and exposing them to 0.5 μM tetrodotoxin, 25 μM NBQX, and 50 μM CPP and were blocked by 50 μM picrotoxin. Data were acquired using either Clampex10 or custom MATLAB software, using either an Axopatch 200B or Multiclamp 700B amplifier, and digitized with a DigiData 1440 data acquisition board (Axon Instruments) or a PCIe-6323 (National Instruments). For measuring miniature postsynaptic currents (minis), cells were allowed to stabilize for at least two minutes. For paired pulse experiments, no drugs were used in the ACSF. A stimulating electrode (ISO-Flex, A.M.P.I.) was positioned approximately 100 μm below the cell, and 0.1 ms electrical pulses were given while adjusting the stimulus intensity and electrode position until the first pulse was between 100 and 500 pA. Inter-stimulus interval was varied and 10 s elapsed between each sweep. Pulse amplitudes were obtained from average sweeps of at least ten trials. Cells were held at 0 mV to record IPSCs and −70 mV to record EPSCs. For evoked IPSCs, no drugs were used in the ACSF. Simultaneous paired whole-cell recordings were obtained from an eGFP-expressing VIP neuron and a morphologically identified pyramidal neuron located not more than five cell bodies away from the VIP neuron. Both cells were held at 0 mV, and a 5 ms light pulse from a blue LED (Thorlabs) was used to evoke IPSCs. Light intensity and the objective position were varied until the VIP neuron IPSC amplitude was between 200 and 500 pA. Average light power at 470 nm varied from between 0.3 and 0.7 mW over the course of the experiment. Reported ratios were obtained by dividing IPSC amplitudes obtained from an average trace of at least ten trials. For electrophysiology experiments, n was set to min n = 10 to detect 20% effect size with power 0.95. For experiments to determine average firing rate of VIP neurons, a modified ACSF that promotes increased action potential firing was used containing, 3.5 mM KCl and 0.8 mM CaCl . Cell-attached patch recordings were obtained from eGFP-positive cells. Cells that did not fire an action potential in the first 30 s of recording were discarded, and recordings were maintained for at least 30 ten-second sweeps. Average firing rate was determined from the first sweep to the last recorded sweep in which an action potential occurred. Miniature IPSC and EPSC data were analysed using Axograph X. Events were identified using a variable amplitude template-based strategy. Templates for each event type were defined as follows: mEPSC: 0.25 ms rise time, 3 ms decay τ, amplitude threshold of −3 × s.d. local noise; mIPSC: 1 ms rise time, 50 ms decay τ, amplitude threshold of 2.5 × s.d. local noise. Local noise was determined by calculating the standard deviation of the current in a 5 ms window before event rise onset. Templates lengths extended 25 ms after rise onset in the case of mEPSCs and 50 ms after rise onset in the case of mIPSCs. Events were discarded if they had a rise time outside the range of 0–3 ms. Statistical significance for all recorded parameters between genotypes was evaluated using a Mann–Whitney U-test on the mean values from individual neurons in a given experiment. Minis were additionally evaluated for significance using both a Kolmogorov–Smirnov test (KS test) and Monte Carlo test. For these tests, 50 random minis were sampled from each neuron in each condition to obtain a continuous distribution for each condition that equally weighted each cell in that condition: these distributions are the data shown in the cumulative distribution graphs. One hundred random events were randomly sampled from these distributions for a KS test; and for Monte Carlo tests, 100 random events were randomly sampled from each distribution 1,000 times (with replacement), and the means were compared. All significant differences in mini amplitude and frequency were found to be significant by Monte Carlo test, KS test, and Mann–Whitney U-test of cell means. Since the Mann–Whitney test was found to be the most stringent test, the P values from Mann–Whitney tests are reported. All data was analysed blind to genotype or experimental condition. In all conditions, series resistance, holding potential, cell capacitance, and input resistance were recorded and were not found to be significantly different except where noted. Statistical tests were performed using Graphpad Prism and MATLAB. VIP neurons were filled with a patch pipette containing 1% Alexa 647 Hydrazide and the internal solution was allowed to dialyze for at least 30 min before slices were fixed in 4% paraformaldehyde for 1 h at room temperature. Slices were then washed three times for 30 min in PBS before slices were mounted in Fluormount-G (Southern Biotech). Images were acquired using a Zeiss Axio Imager microscope with a 20× objective with the use of an Apotome (Zeiss). Neurons were reconstructed using NeuronJ (ImageJ), and Sholl analysis was performed using a custom script in MATLAB. Eyelids were trimmed and sutured under isoflurane anaesthesia (1–2% in O ) as previously described31. The integrity of the suture was checked daily and mice were used only if the eyelids remained closed throughout the duration of the deprivation period. One eye was closed for 4 days starting between P26 to P28. The eyelids were reopened immediately before recording, and the pupil was checked for clarity. VEPs were recorded from anaesthetized mice (50 mg kg−1 Nembutal and 0.12 mg chlorprothixene) using standard techniques described previously32. The contra- and the ipsilateral eye of the mouse were presented with horizontal black and white sinusoidal bars that alternated contrast (100%) at 2 Hz. A tungsten electrode was inserted into the binocular visual cortex at 2.8 mm from the midline where the visual receptive field was approximately 20° from the vertical meridian. VEPs were recorded by filtering the signal from 0.1–100 Hz and amplifying 10,000 times. VEPs were measured at the cortical depth where the largest amplitude signal was obtained in response to a 0.05 c.p.d. stimulus (400–600 μm); 3–4 repetitions of 20 trials each were averaged in synchrony with the abrupt contrast reversal. The signal was baseline corrected to the mean voltage of the first 50 ms post-stimulus-onset. VEP amplitude was calculated by finding the minimum voltage (negative peak) within a 50–150 ms post-stimulus-onset time window. Acuity was calculated only from the deprived eye. For each different spatial frequency, 3–4 repetitions of 20 trials each were averaged in synchrony with the abrupt contrast reversal. VEP amplitude was plotted against the log of the different spatial frequency, and the threshold of visual acuity was determined by linear extrapolation to 0 μV. Igf1 conditional knockout mice15, Ai9 tdTomato reporter mice33, Emx1-cre34, Pv-cre35, Gad2-cre, Sst-cre, Vip-cre36 and RiboTag mice8 are available from The Jackson Laboratory. For routine experimentation, animals were genotyped using a PCR-based strategy; PCR primer sequences are available at the The Jackson Laboratory’s website. For RiboTag experiments, mice homozygous for the RiboTag allele were crossed to mice homozygous for the cre allele and all experiments were performed with mice double heterozygous for both the RiboTag and the cre alleles. For Igf1 cKO experiments, mice heterozygous for the Igf1 conditional allele (Igf1fl/WT) and homozygous for the Vip-cre allele were crossed to mice heterozygous for the Igf1 conditional allele and homozygous for the tdTomato reporter allele. Resulting littermates all had one copy of the Vip-cre transgene and the tdTomato Cre reporter and yielded Igf1WT/WT and Igf1fl/fl littermates for experimentation. For injections of AAV constructs in the visual cortices of Cre mice (Vip-, Pv-, Sst-, or Emx1-cre), mice homozygous for the cre allele were crossed to wild-type C57Bl6 mice and offspring heterozygous for the cre allele were used for experiments. The use of animals was approved by the Animal Care and Use Committee of Harvard Medical School and/or the University of California Berkeley.