Evaluating Stage Motion for Automated Electron Microscopy.

Precise control is an essential and elusive quality of emerging self-driving transmission electron microscopes (TEMs). It is widely understood these instruments must be capable of performing rapid, high-volume, and arbitrary movements for practical self-driving operation. However, stage movements are difficult to automate at scale, owing to mechanical instability, hysteresis, and thermal drift. Such difficulties pose major barriers to artificial intelligence-directed microscope designs that require repeatable, precise movements. To guide design of emerging instruments, it is necessary to understand the behavior of existing mechanisms to identify rate limiting steps for full autonomy. Here, we describe a general framework to evaluate stage motion in any TEM. We define metrics to evaluate stage degrees of freedom, propose solutions to improve performance, and comment on fundamental limits to automated experimentation using present hardware.

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics.

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies.

A high-throughput approach and platform using 15 min reversed-phase capillary liquid chromatography (RPLC) separations in conjunction with ion mobility spectrometry-mass spectrometry (IMS-MS) measurements was evaluated for the rapid analysis of complex proteomics samples. To test the separation quality of the short LC gradient, a sample was prepared by spiking 20 reference peptides at varying concentrations from 1 ng/mL to 10 microg/mL into a tryptic digest of mouse blood plasma and analyzed with both a LC-Linear Ion Trap Fourier Transform (FT) MS and LC-IMS-TOF MS. The LC-FT MS detected 13 out of the 20 spiked peptides that had concentrations >or=100 ng/mL. In contrast, the drift time selected mass spectra from the LC-IMS-TOF MS analyses yielded identifications for 19 of the 20 peptides with all spiking levels present. The greater dynamic range of the LC-IMS-TOF MS system could be attributed to two factors. First, the LC-IMS-TOF MS system enabled drift time separation of the low concentration spiked peptides from the high concentration mouse peptide matrix components, reducing signal interference and background, and allowing species to be resolved that would otherwise be obscured by other components. Second, the automatic gain control (AGC) in the linear ion trap of the hybrid FT MS instrument limits the number of ions that are accumulated to reduce space charge effects and achieve high measurement accuracy, but in turn limits the achievable dynamic range compared to the IMS-TOF instrument.

Fully automated four-column capillary LC-MS system for maximizing throughput in proteomic analyses.

We describe a four-column, high-pressure capillary liquid chromatography (LC) system for robust, high-throughput liquid chromatography-mass spectrometry (LC-MS(/MS)) analyses. This system performs multiple LC separations in parallel, but staggers each of them such that the data-rich region of each separation is sampled sequentially. By allowing nearly continuous data acquisition, this design maximizes the use of the mass spectrometer. Each analytical column is connected to a corresponding ESI emitter in order to avoid the use of postcolumn switching and associated dead volume issues. Encoding translation stages are employed to sequentially position the emitters at the MS inlet. The high reproducibility of this system is demonstrated using consecutive analyses of global tryptic digest of the microbe Shewanella oneidensis.

High speed method for in situ multispectral image registration.

High speed data registration is required for the study of fluorescence resonance energy transfer in real time as well as fast dynamic intra- and inter-cellular signaling events. Multispectral confocal spinning disk microscopy provides a high resolution method for performing such real time live cell imaging. However, optical distortions and the physical misalignments introduced by the use of multiple acquisition cameras can obscure spatial information contained in the captured images. In this manuscript, we describe a multispectral method for real time image registration whereby the image from one camera is warped onto the image from a second camera via a polynomial correction. This method provides a real time pixel-for-pixel match between images obtained over physically distinct optical paths. Using an in situ calibration method, the polynomial is characterized by a set of coefficients, using a least squares solver. Error analysis demonstrates optimal performance results from the use of cubic polynomials. High-speed evaluation of the warp is then performed through forward differencing with fixed-point data types. Forward differencing is an iterative approach for evaluating polynomials on the condition that the function variable changes with constant steps. Image reconstruction errors are reduced through bilinear interpolation. The registration techniques described here allow for successful registration of multispectral images in real time (exceeding 15 frame/s) and have a broad applicability to imaging methods requiring pixel matching over multiple data channels.