Gatan Digital Micrograph Crack
Tisham Candella
My questions are,
1. when I specify "Exposure per frame", shall I use the value readout by the camera, received by the camera, or received by the specimen?
2. the filter formula and constants are derived based on the beam intensity that gives 8 electrons/pixel/s on the camera. Do you think it is applicable to data collected using much lower/higher beam intensity?
3. Can we use the same exposure filter function for Falcon or DE data?
1. "Exposure" refers to the number of electrons incident on the specimen, per unit area
3. The filter formula concerns the damage of protein under electron exposure, it is independent of the detector.
2. I'm not aware of reports that describe protein damage due to electron exposure where the amount/rate of damage is a function of the exposure rate (number of electrons per unit time), so I would guess that the exposure filter applies regardless of beam
intensity. But others may have more knowledge of this matter than I do.
You raise a very interesting point here. We used a value of 100e-/A2 under the assumption it were the exposure that the specimen was exposed to. It is my understanding that the version of digital micrograph that we used corrected for the coincidence loss etc and thus the reported value is in (corrected) electrons and not detected counts (therefore we state 8 electrons per pixel in the paper - not counts). The critical exposure curves, and the dose filter curves are plotted under this assumption - i.e. that the viruses were exposed to 100e/A2 in total. We thus used a value of 53e-/A2 for the NRAMM proteosome data assuming this is the corrected value and thus an approximate estimate for the exposure of the specimen.
I have now double checked this with Chris Booth from Gatan - he confirmed that the adjustments are done internally by Digital Micrograph, thus the exposure values we quote in the paper should be approximately equal to the exposure the sample receives.
Reviewed by: Sonja-Verena Albers, University of Freiburg, Germany; Luis Raul Comolli, ALS-Molecular Biology Consortium and Lawrence Berkeley National Laboratory, USA; Ariane Briegel, California Institute of Technology, USA
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In the course of evolution, nature has developed simple and fascinating solutions for various challenges. Particularly microbial life seems thus to harbor an enormous potential of exploitable biomaterial, such as enzymes and other biomolecules. These compounds are thought to prove very useful for diverse applications, in e.g., medicine, pharmacy, or industry (e.g., Beg et al., 2001; Hasan et al., 2006; Dutta and Kundu, 2014). However, the majority of naturally occurring exploitable biomaterial remains to be explored, because a substantial amount of microorganisms resist cultivation in the laboratory and thus escape detailed characterization of their metabolic potential and functional traits.
Cultivation-independent methods such as metagenomics enable scientists to directly access the genetic information of (entire) microbial communities. The sequence information retrieved can be used for assembly of near complete to complete genomes from key or underrepresented members of the communities (Tyson et al., 2004; Sharon and Banfield, 2013; Sharon et al., 2013). This information thus provides the basis for functional annotation of these novel microbial genomes. However, annotation of genes from lineages with only distant representatives is sometimes limited. Some cases have been reported in which approximately 50% of the predicted proteins could not be assigned a function (Baker et al., 2010; Kantor et al., 2013). Consequently, linking metagenomic data from uncultivated microorganisms with information retrieved by other molecular methods and/or imaging techniques in order to characterize such unknown predicted proteins is a promising approach. Imaging techniques, however, can currently not be conducted for highly complex microbial communities (e.g., those from soil) without substantial loss of information. Nevertheless, populations with low and simple diversity and uneven abundance of its members, such as the uncultivated acid mine drainage microbial community, can be studied in detail using a variety of these techniques, enabling researchers to link metagenomics to cellular characteristics (Comolli et al., 2009; Baker et al., 2010; Yelton et al., 2013; Comolli and Banfield, 2014).
In this study a combination of -omic techniques with electron microscopy was applied in order to identify the bona fide gene sequence of the hamus subunit, shed light onto its phylogenetic evolution and further analyze its structure and the membrane in which it is anchored. Due to its barbed-wire-like structure and in particular its distal nano-hook, the hamus is considered an exploitable biomaterial and thus a tool for nanobiotechnology (Moissl-Eichinger et al., 2012), for which we provide the basis in this communication.
Archaeal biofilms were washed from the nets, and free-floating biofilm pieces were collected onto on gold-plated screens (hole 100 μm, G225G1, Plano GmbH, Wetzlar, Germany). Samples were immediately dried and the gold-coated aperture disks were placed onto silicon wafers and affixed along the edges with adhesive tape, with care to avoid contact with the biofilm.
For analysing the cell surface appendages, the unfixed, purified hami were deposited on a carbon-coated copper grid and negatively stained with uranylacetate [2% (w/v), pH 4.5]. The samples were examined with a CM12 transmission electron microscope (FEI Co., Eindhoven, The Netherlands) operated at 120 kV. All images were digitally recorded using a slow-scan CCD camera that was connected to a computer with TVIPS software (TVIPS GmbH, Gauting, Germany). Scanning electron microscopy was performed as described in Probst et al. (2014).
For element analysis of the particles within coccoid cells, Ca. A. hamiconexum biofilm flocks were embedded in TAAB embedding resin (TAAB, Aldermaston, UK) and thin sectioned as described in Milić et al. (2015) followed by staining with platinum blue and lead citrate. Energy Filtered TEM (EFTEM) was performed with a Gatan GIF Quantum 963 energy filter using an FEI Tecnai 20 microscope at 120 kV acceleration voltage. To visualize the elemental distributions, elemental maps were made using the three window method at the standard losses provided by Gatan Digital Micrograph software (see also Teubl et al., 2014).
Energy Dispersive X-Ray spectroscopy was performed using an Edax silicum type ultrathin unit (SUTW) detector, as described in Milić et al. (2015); the corresponding images were made with scanning transmission EM using a High Angle Annular Dark Field detector (HAADF).
For the production of hamus-specific antibodies for protein analyses and structural investigations, hamus filaments were released from the archaeal cell surface. The purification procedure, as well as the production of hamus-specific antibodies, has been described elsewhere (Probst et al., 2014). In brief, the archaeal biofilm cells were lysed using 0.1% (w/v) sodium dodecyl sulfate (SDS) and cell debris was removed via subsequent centrifugation and sucrose-gradient centrifugation steps.
The separated proteins were afterwards semi-dry-blotted onto a Roti-PVDF (polyvinylidene fluoride) membrane (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) using a semidry transfer cell instrument (Bio-Rad, Munich, Germany) operated at 16 V for 1 h. Blocking was performed by incubation of the membrane in Tris buffered saline [including Tween 200.01% (v/v), 3% milk powder (w/v); TBST-B] overnight. After a washing step with Tris buffered saline [including Tween 200.01% (v/v); TBST], the primary antibody (anti-hamus) was applied (1:5,000 dilution in TBST-B) and incubated for 3 h under agitation. The membrane was washed using TBST and the secondary antibody [anti-chicken coupled with horseradish peroxidase (1:1,000 in TBST-B; Sigma-Aldrich Chemie GmbH, Munich, Germany)], was applied for 2 h]. The reaction was visualized by applying a 3-amino-9-ethylcarbazole solution [20 mg of 3-amino-9-ethylcarbazole dissolved in 1 ml ethanol p.a., followed by mixing with 50 ml of potassium acetate, pH 5, 20 mM, 100 μl of triton X-100, 10% (v/v) and 10 μl of H2O2] after another washing step.
For identification of peptides in the band showing positive reaction in the western blot analysis, the corresponding band in the SDS-PAGE was cut out and trypsin digested. Obtained peptides were then subjected to liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). HPLC was carried out using a Ultimate3000 RSLC nano-HPLC System (Thermo Fisher Scientific; at the facilities of Prof. Dr. R. Deutzmann, University of Regensburg) with a reversed phase chromatography analytical column (ReproSil Pur 120 C18-AQ, 75 μm 25 cm). The mobile phase consisted of a linear gradient containing 0.1% (v/v) formic acid (eluent A) and 80% (v/v) acetonitrile, 0.1% (v/v) formic acid (eluent B). HPLC was coupled on-line to a maXis plus UHR-QTOF system (Bruker Daltonics) via nano-electrospray source and up to five most abundant precursors selected for fragmentation by collisional induced dissociation (CID). Identification of the obtained peptide mass fingerprints was performed by genome database searching using the PeptideMass software (Wilkins et al., 1997).
The trans-membrane region of the hamus protein was predicted by TMHMM v2.0 ( ). The protein characteristics were analyzed using GenScript's Peptide Property Calculator ( -bin/site2/peptide_calculation.cgi) and by NetNGlyc 1.0 ( ).
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