Maintaining Microclimates during Nanoliter Chemical Dispensations Using Custom-Designed Source Plate Lids

Foley, Bryan J.; Drozd, Ashley M.; Bollard, Mary T.; Laspina, Denise; Podobedov, Nikita; Zeniou, Nicholas; Rao, A.R.; Andi, Babak; Jackimowicz, R.; Sweet, Robert M.; McSweeney, Seán; Soares, Alexei S.

doi:10.1177/2211068215616072

Cited by 4 publications

(3 citation statements)

References 32 publications

(31 reference statements)

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“…M3-L18SP-10) so that none of the droplets was in contact with any other. Crystal-containing droplets were threaded through small apertures to prevent cross-contamination (Foley et al, 2016). We then swept the non-crystal-containing side of the micro-mesh against a sponge moistened with cryoprotectant (mother liquor + 20% glycerol) and, in one smooth motion, immediately cryocooled the micro-mesh in liquid nitrogen.…”

Section: Limits Of Conventional Clusteringmentioning

confidence: 99%

Serial crystallography with multi-stage merging of thousands of images

et al. 2022

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KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

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Section: Limits Of Conventional Clusteringmentioning

confidence: 99%

Serial crystallography with multi-stage merging of thousands of images

et al. 2022

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“…We used acoustic sound pulses to harvest 2.5nL of crystal slurry from each of the four lysozyme aliquots, and separately position them on a micromesh (MiTeGen M3-L18SP-10) such that none of the droplets was in contact with any other ( Figure 1A). Crystal containing droplets were threaded through small apertures to prevent cross-contamination (Foley et al, 2016). We then swept the non-crystal containing side of the micro-mesh against a sponge moistened with cryo-protectant (mother liquor + 20% glycerol) and, in one smooth motion, immediately cryo-cooled the micromesh in LN 2 .…”

Section: Limits Of Conventional Clusteringmentioning

confidence: 99%

Serial Crystallography with Multi-stage Merging of 1000s of Images

Andrews²,

Foadi

et al. 2017

Preprint

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KAMO and Blend provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options to increase the sensitivity to differences in unit cell parameters and to allow for clustering of nearly complete datasets on the basis of intensity or amplitude differences. If datasets are already sufficiently complete to permit it, apply KAMO once, just for reflections. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step either the simple cell vector distance of the original Blend is used, or the more sensitive NCDist, to find clusters to merge to achieve sufficient completeness to allow intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections at the common HKLs to merge clusters in a way sensitive to structural differences that may not perturb the cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which can then be merged to, hopefully, yield the electron density from a single protein iso-form. What is striking about many of these physical parameters is that they are largely independent from one another. Consequently, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy was to use unit-cell clustering until merged data was of sufficient completeness to then use intensity based clustering. We have demonstrated that, using this strategy, we were able to accurately cluster data sets from crystals that had subtle differences.

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“…However, the result of this method is not stable and would be invalid for long-term cultivation (>24 h). Furthermore, a special anti-volatile lid has been developed to restrain the edge effect, 15 but the performance is limited. Malo and Hanley 16 proposed a statistical method to optimize the conguration of the reference wells for reducing the positional effects of wells within plates.…”

Section: Introductionmentioning

confidence: 99%

Edge effect detection for real-time cellular analyzer using statistical analysis

Chen

Pan

et al. 2017

RSC Adv.

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Real time cellular analyzers (RTCA) are widely used to test the cytotoxicity of chemicals. However, there are some uncontrollable factors, which are detrimental to the experimental quality. One of the fundamental issues is the edge effect. Abnormal time-dependent cellular response curves (TCRCs) are observed when the wells are located at the edge of the E-plate. In this paper, the Smirnov test was used to detect the edge effect. The average normalized cell index (NCI) of the negative control located in the inner wells was taken as the standard. Thereafter, all TCRCs were divided into several intervals, and their corresponding empirical distribution functions (EDFs) of the mean NCI and NCI located at the edge wells were calculated, and hypothesis testing was used to determine the differences of the EDFs. The experimental results evaluated the performance of the proposed algorithm. This framework provided a systematic method for edge-effect detection.

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Maintaining Microclimates during Nanoliter Chemical Dispensations Using Custom-Designed Source Plate Lids

Cited by 4 publications

References 32 publications

Serial crystallography with multi-stage merging of thousands of images

Serial crystallography with multi-stage merging of thousands of images

Serial Crystallography with Multi-stage Merging of 1000s of Images

Edge effect detection for real-time cellular analyzer using statistical analysis

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