H. Piyadasa was supported by the Canadian Institutes of Health Research (CIHR) Fellowship (MFE-176490). B. Oberlton was supported by the National Science Foundation (NSF) Fellowship (2020298220). A. Tsai was supported by a Damon Runyon Cancer Research Foundation (DRCRF) Fellowship (DRG-118-16), the Stanford Department of Pathology, the Annelies Gramberg Fund, and NIH 1U54HL165445-01. Additional acknowledgments go to Dr. Avery Lam, Dr. Davide Franchina, and Mako Goldston for helping to test and debug the program.

1. Loading of TSAI
<ol>
	<li>Run TSAI by opening&#160;https://tsai.stanford.edu/research/mibi_tsai in the web browser of the MIBI user control computer.
	<ol>
		<li>This instance of TSAI contains custom presets which do not apply to all instruments. When using it, build tiles only from template FOV(s) as generated&#160;below in step 2.6. TSAI runs locally within the web browser, and no image, .json, or file name data is sent to or stored on the server.</li>
	</ol>
	</li>
	<li>Alternatively, set up TSAI on any website with custom presets for any instrument.
	<ol>
		<li>Go to https://github.com/ag-tsai/mibi_tsai and download the mibi_tsai_standalone directory. Alternatively, download the&#160;Supplementary Coding File 1 .zip file and unzip the contents to a directory titled mibi_tsai_standalone.</li>
		<li>Open mibi_tsai_standalone/_resources/index.js in any text editor.</li>
		<li>If necessary, edit the FOV size, dwell time/timing choice, raster size, FOV JSON, and recommended preset settings in index.js to match the instrument&#39;s settings. This mainly applies to customized instruments, but dwell time/timing choice pairs should be checked regardless. Save index.js.</li>
		<li>Upload mibi_tsai_standalone to any web server accessible through the internet, e.g., a lab web site or university-hosted web site.</li>
		<li>Open mibi_tsai_standalone/index.html in the web browser of the MIBI user control computer.</li>
	</ol>
	</li>
</ol>
2. Loading the MIBI slide and creating a template file
<ol>
	<li>Log into the MIBI experiment tracker (manufacturer-provided web interface for managing scan-related metadata) in the web browser.</li>
	<li>In the Slides tab, add&#160;a new slide and add a new section (Supplementary Figure 2A-B). In the Resources tab, select or create a marker panel (Supplementary Figure 2C).</li>
	<li>In the Sections tab, add the new section to the panel (Supplementary Figure 2D).</li>
	<li>Log into the MIBI user control interface in the web browser. Load the MIBI slide by clicking Exchange Sample and selecting the new slide (Supplementary Figure 3A).</li>
	<li>Create a template FOV by clicking Add FOV (Supplementary Figure 3B) and setting the frame dimensions, FOV size, dwell time, imaging mode, and section ID.</li>
	<li>Export (download) the FOV list to a .json file (Supplementary Figure 3C). Download the optical image as a .png file (Supplementary Figure 3D).</li>
</ol>
3. Optical image-stage motor coregistration
<ol>
	<li>Open the TSAI web UI in the web browser. If coregistration has not been previously performed, the optical coregistration menu should open automatically. If it has been performed and is adequate, do not repeat these steps.</li>
	<li>Open the Optical Coregistration menu. Click Copy Automatic Coregistration Code to Clipboard (Supplementary Figure 4A).</li>
	<li>Open the MIBI user control interface in the web browser. Press Ctrl+Shift+J to open the browser console, or right-click on the page and click Inspect, then open the Console tab (Supplementary Figure 4B).</li>
	<li>Paste the code into the console and press Enter. Click the link generated in the console (Supplementary Figure 4C). This will load the coregistration into the TSAI web UI and save it as a cookie, so it persists and does not need to be repeated unless there is a change to the instrument hardware.</li>
</ol>
4. Tiled SED scan
<ol>
	<li>Load the optical image .png and .json files from step&#160;2.6&#160;by dragging and dropping them onto the TSAI web UI.</li>
	<li>Open the SED Tiler menu and click on a text box in the top row (Supplementary Figure 5A).</li>
	<li>Click (&#177;&#160;drag) on the optical image to select the top left corner for the SED scan (Supplementary Figure 5B).</li>
	<li>Press the D key or click on a text box in the second row in the SED Tiler menu.</li>
	<li>Click (&#177;&#8203;&#160;drag) on the optical image to select the bottom right corner for the SED scan.</li>
	<li>In the SED Tiler menu, click Copy SED Scan and Shift Correction Code to Clipboard (Supplementary Figure 5C).</li>
	<li>Open the MIBI user control interface in the web browser. Paste the code into the console and press Enter (Supplementary Figure 5D).</li>
	<li>Put the MIBI into SED mode on the QC - 300 &#181;m setting, move to an area that will not be acquired, and adjust the gain, focus, and stigmation.
	<ol>
		<li>Adjust the brightness and contrast of the SED image without changing the gain. Press B to increase the brightness or Shift+B to decrease it. Press C to increase the contrast or Shift+C to decrease it. Press Shift+V to reset both the brightness and contrast.</li>
	</ol>
	</li>
	<li>Press Shift+T to start the tiled SED scan.</li>
	<li>When finished, it should automatically save a new .png file of the tiled SED image (Figure 3). Characters may be added to the beginning of the file name but do not modify any other part of the file name.</li>
	<li>If specific tiles are out of focus or otherwise improperly scanned, rescan them.
	<ol>
		<li>Press Shift+R to add a tile to the rescan queue. A dialog box will open, prompting the user for the row and column of the tile. The numbers are zero-indexed, thus entering 8,0 queues the ninth row, the first column.</li>
		<li>After adding all the relevant tiles to the queue, press Shift+T to rescan. When finished, it should automatically save a new .png file of the tiled SED image.</li>
	</ol>
	</li>
	<li>Critical step: Inspect the tiled SED scan for large misalignments (Figure 3C-D). If present, contact manufacturer support to adjust the motor and imaging beam or attempt manual software correction using the keyboard controls in steps 4.12.1 to 4.12.9 (Supplementary Figure 6A).
	<ol>
		<li>To check SED-stage motor alignment, move to an area of the slide without tissue. Press Shift+5 to burn five 400 &#181;m FOVs in a checkerboard pattern (Supplementary Figure 6B-C) or Shift+9 to burn a 3 x 3 pattern of 400 &#181;m FOVs (Supplementary Figure 6D-E).</li>
		<li>If the FOV columns are too far apart, press 1 and set the x f(x) value to a negative decimal, typically between -0.0025 and -0.1.</li>
		<li>If the third-row FOVs are shifted leftward relative to the first-row FOVs, press 2 and set the x f(y) value to a positive decimal, typically between 0.0025 and 0.1.</li>
		<li>If the third column FOVs are shifted downward relative to the first column FOVs, press 3 and set the y f(x) value to a negative decimal, typically between -0.0025 and -0.1.</li>
		<li>If the FOV rows are too far apart, press 4 and set the y f(y) value to a negative decimal, typically between -0.0025 and -0.1.</li>
		<li>Iteratively repeat steps 4.12.1 to 4.12.5 until the checkerboard and 3 x 3 patterns form a roughly straight grid (Supplementary Figure 6C, E).</li>
		<li>Press S to save a .png image of the pattern with the correction values in the file name.</li>
		<li>Drag and drop this .png file onto the TSAI web UI to load the values and save them to the browser cookie.</li>
		<li>Perform tiled SED scans to check the coefficients. Based upon the same principles as in steps 4.12.2 to 4.12.5, make further adjustments to the coefficients to correct any misalignments in the tiled SED images.</li>
	</ol>
	</li>
	<li>If the tiled SED is adequate, press Escape. Return to the TSAI web UI.</li>
	<li>Drag and drop the tiled SED .png file onto the TSAI web UI (Supplementary Figure 5E).</li>
	<li>Click on the SED tab and adjust the zoom (Supplementary Figure 5F).</li>
	<li>To adjust image brightness and contrast and/or drawing options such as line thickness and cursor size, use the slide options menu above the SED image.</li>
	<li>Keyboard shortcuts are available and most shown next to the image controls: Press Z to zoom in and Shift+Z to zoom out. Press B to increase brightness or Shift+B to decrease it. Press C to increase contrast or Shift+C to decrease it. Press Shift+V to reset both the brightness and contrast. Press L to toggle labels above the tiles. Press O to toggle 5 mm radius circles drawn around focus sites.</li>
</ol>
5. Tissue microarray (TMA)
<ol>
	<li>If setting FOVs for a grid of TMA spots, first set the pattern of FOVs to be replicated. In the relevant tile of the Tiles column, adjust the columns and rows (Figure 4A) and check/uncheck the boxes in the map (Figure 4B), as well as adjusting other FOV settings as necessary.</li>
	<li>In the relevant tile, click TMA to open the TMA options menu (Figure 4C). Set the number of rows and columns of TMA spots (Figure 4D). If necessary, add a naming prefix (Figure 4E) and edit the starting row and column numbering (Figure 4F).</li>
	<li>On the slide image, click on the four corners of the TMA (Figure 4G-J). Click and drag the circled corners to adjust the positioning of the crosshairs so they best match the TMA spots.</li>
	<li>Click Build TMA from the TMA options menu (Figure 4K).</li>
	<li>Hover over each tile in the tiles column to check its positioning. To adjust, click Move (Figure 4L). Then click and drag on the slide image or press the keyboard arrow keys.
	<ol>
		<li>Hold the Shift key while pressing arrow keys to move a farther distance. Hold the Alt (Windows) or Opt (Mac) key while pressing arrow keys to move a shorter distance.</li>
		<li>When move is selected, press T to uncheck the checkbox next to the tile name, remove it from view, and omit it from any subsequently generated .json files. Alternatively, uncheck the checkbox directly with the mouse (Figure 4M) or remove it entirely by clicking Delete.</li>
		<li>When move is selected, press 2, 4, or 8 to set the FOV size to 200 &#181;m, 400 &#181;m, or 800 &#181;m, respectively, and the raster dimensions will be scaled proportionately such that the imaging resolution remains unchanged.</li>
		<li>When move is selected, press A to go to the previous tile or&#160;press D to go to the next tile.</li>
		<li>To adjust other tile settings, click&#160;the&#160;&#8801; button to expand the settings menu if it is not visible.</li>
	</ol>
	</li>
</ol>
6. Area/polygon tile
<ol>
	<li>If setting FOVs to cover a contiguous area of tissue, first adjust FOV settings as necessary in the relevant tile of the Tiles&#160;column.</li>
	<li>In the relevant tile, click Polygon (Figure 5A). Click on the slide image to set the vertices/corners of the area to be tiled (Figure 5B-C). Double-click to close the polygon and cover the area with FOVs (Figure 5D).</li>
	<li>Scroll to the bottom of the Tiles&#160;column and click the&#160;&#8801; button (^ when expanded, Figure 5E) in the new polygon tile to see the tile map.</li>
	<li>Toggle individual tiles ON or OFF by clicking on the tile map (Figure 5F), or by clicking Clicker (Figure 5G) and clicking on the tiled FOVs in the slide image.</li>
	<li>To toggle off multiple FOVs, click on Eraser and then click and drag on the tiled FOVs in the slide image (Figure 5H).</li>
	<li>To toggle on multiple FOVs, click on Clicker (Figure 5G) and then click and drag on the empty areas in the slide image covered by the tile map.</li>
	<li>To insert the rows above, click the &#9650; button (Figure 5I). To insert columns to the left, click the &#9668; button (Figure 5J).</li>
	<li>To adjust tile positioning, click Move (Figure 5K). Then click and drag on the slide image, press the keyboard Arrow keys, or use other controls described in steps 5.5.1 to 5.5.5.</li>
</ol>
7. FOV navigation and adjustment
<ol>
	<li>If SED tiling is misaligned or the optical image crosshair does not reflect the actual stage motor position, adjust FOV positions in SED mode in the MIBI user control interface with the aid of the below keyboard controls.</li>
	<li>Open the FOV navigation/adjustment menu below the slide (optical or SED) image. Click Copy FOV Navigation Code to Clipboard.</li>
	<li>Open the MIBI user control interface in the web browser. Put the MIBI into SED mode and adjust the gain, focus, and stigmation.</li>
	<li>Press Ctrl+Shift+J to open the browser console, or right-click on the page and click Inspect, then open the Console tab.</li>
	<li>Paste the code into the console and press Enter. The code will automatically navigate to the first FOV and the exact FOV positioning displayed in the SED image of the MIBI user control interface.</li>
	<li>Adjust the brightness and contrast of the SED image without changing the gain. Press B to increase the brightness or Shift+B to decrease it. Press C to increase the contrast or Shift+C to decrease it. Press Shift+V to reset both the brightness and contrast.</li>
	<li>To adjust the SED magnification, press M (200 &#181;m), , (400 &#181;m), . (800 &#181;m), or / (maximum) keys.</li>
	<li>To move the FOV, press the keyboard Arrow keys. Save the position by pressing W. Hold the Shift key while pressing arrow keys to move a farther distance. Hold the Alt (Windows) or Opt (Mac) key while pressing arrow keys to move a shorter distance. Note that only R1C1 of any given tile can be moved.</li>
	<li>To toggle an FOV ON or OFF, press T. To change the FOV size, press 2 (200 &#181;m), 4 (400 &#181;m), or 8 (800 &#181;m). The raster dimensions will be scaled proportionately such that the imaging resolution remains the same.</li>
	<li>To save an image file of the SED image and overlaid crosshair, press S. To save a draft of the adjustments to a .txt file, press X.</li>
	<li>When satisfied, press D to go to the next FOV, or A to go back to the previous FOV. Repeat steps 7.6&#160;to 7.11&#160;for all FOVs.</li>
	<li>When finished with all FOVs, press X or Escape. Adjustments will be saved to a .txt file and copied to the clipboard.</li>
	<li>Return to the TSAI web UI. Drag and drop the .txt file onto the TSAI web UI or paste the adjustments into the text box in the FOV navigation/adjustment menu.</li>
	<li>Click Adjust to apply adjustments to the tiles in the Tiles&#160;column.</li>
</ol>
8. JSON file generation and import
<ol>
	<li>Below the Tiles&#160;column, under Output, check the list of tiles and estimated run time (Supplementary Figure 7A).</li>
	<li>Under Group, select an option for FOV grouping (Supplementary Figure 7B). Grouping has no effect on the sequentially ordered .json file.
	<ol>
		<li>For the randomized .json file, grouping FOVs by tile will order FOVs such that all the FOVs within a given tile stay together, even though the tiles are in random order.</li>
		<li>For the randomized .json file, do not group FOVs will randomly order FOVs such that FOVs from different tiles are intermixed.</li>
		<li>If in-run autofocus has been specified, FOVs will automatically be grouped by the closest autofocus site.</li>
	</ol>
	</li>
	<li>Under split, select an option for splitting into multiple .json files (Supplementary Figure 7C).
	<ol>
		<li>Do not split will keep all FOVs in only one .json file.</li>
		<li>Split by every # FOVs will split FOVs across multiple .json files, where each file contains the specified number of FOVs.</li>
		<li>Split by every # hours # minutes will split FOVs across multiple .json files, where each file&#39;s estimated run time is roughly the specified amount of time.</li>
	</ol>
	</li>
	<li>View and rearrange the order of FOVs in the .json files by opening the Rearrange menu (Supplementary Figure 7D). To move an FOV, click and drag it to the desired position. The other FOVs will interactively rearrange around the dragged FOV.</li>
	<li>To save the .json file(s), click on the FOVs button(s) beneath the rearrange menu. The sequential .json puts FOVs in order by tile, then row, then column (Supplementary Figure 7E). The random .json randomizes FOVs within the groups as selected in step 8.2 (Supplementary Figure 7F).</li>
	<li>To save an image of the tissue with the FOVs and applied display options (tile labels, brightness, contrast, etc.), click Save Tiled Image (Supplementary Figure 7G). This is often useful for record-keeping and sharing with collaborators.</li>
	<li>Return to the MIBI user control interface. Click Import FOVs and select the generated .json file. Adjust the focus, stigmation, and current as necessary and click Start Run.</li>
</ol>

Accelerating MIBI Run Setup Using TSAI for Tissue Microarray Imaging

<ol>
	<li>Liu, C. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+Ion+Beam+Imaging:+Insights+into+Pathobiology.">Multiplexed Ion Beam Imaging: Insights into Pathobiology.</a> Annu Rev Pathol. 17, 403-423 (2022).</li><li>Keren, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MIBI-TOF:+A+multiplexed+imaging+platform+relates+cellular+phenotypes+and+tissue+structure.">MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure.</a> Sci Adv. 5 (10), 1-16 (2019).</li><li>Elhanani, O., Keren, L., Angelo, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Dimensional+Tissue+Profiling+by+Multiplexed+Ion+Beam+Imaging.">High-Dimensional Tissue Profiling by Multiplexed Ion Beam Imaging.</a> Methods Mol Biol. 2386, 147-156 (2022).</li><li>Greenwald, N. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole-cell+segmentation+of+tissue+images+with+human-level+performance+using+large-scale+data+annotation+and+deep+learning.">Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.</a> Nat Biotechnol. 40 (4), 555-565 (2022).</li><li>Risom, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transition+to+invasive+breast+cancer+is+associated+with+progressive+changes+in+the+structure+and+composition+of+tumor+stroma.">Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma.</a> Cell. 185 (2), 299.e18-310.e18 (2022).</li><li>McCaffrey, E. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+immunoregulatory+landscape+of+human+tuberculosis+granulomas.">The immunoregulatory landscape of human tuberculosis granulomas.</a> Nat. Immunol. 23 (2), 318-329 (2022).</li><li>Greenbaum, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+spatially+resolved+timeline+of+the+human+maternal&#8211;fetal+interface.">A spatially resolved timeline of the human maternal&#8211;fetal interface.</a> Nature. 619 (7970), 595-605 (2023).</li><li>Hartmann, F. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+metabolic+profiling+of+human+cytotoxic+T+cells.">Single-cell metabolic profiling of human cytotoxic T cells.</a> Nat Biotechnol. 39 (2), 186-197 (2021).</li><li>Patwa, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+imaging+analysis+of+the+tumor-immune+microenvironment+reveals+predictors+of+outcome+in+triple-negative+breast+cancer.">Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer.</a> Commun Biol. 4 (1), 852 (2021).</li><li>Keren, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Structured+Tumor-Immune+Microenvironment+in+Triple+Negative+Breast+Cancer+Revealed+by+Multiplexed+Ion+Beam+Imaging.">A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging.</a> Cell. 174 (6), 1373.e19-1387.e19 (2018).</li><li>Vijayaragavan, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+spatial+proteomic+imaging+for+human+neuropathology.">Single-cell spatial proteomic imaging for human neuropathology.</a> Acta Neuropathol. Commun. 10 (1), 158 (2022).</li><li>. GitHub - angelolab/toffy: Scripts for interacting with and generating data from the commercial MIBIScope. (n.d.) Available from: <a target="_blank" href="https://github.com/angelolab/toffy">https://github.com/angelolab/toffy</a> (2023)</li><li>. HTML Living Standard Available from: <a target="_blank" href="https://html.spec.whatwg.org/multipage">https://html.spec.whatwg.org/multipage</a> (2023)</li><li>. ECMAScript 2022 Language Specification Available from: <a target="_blank" href="https://www.ecma-international.org/publications-and-standards/standards/ecma-262">https://www.ecma-international.org/publications-and-standards/standards/ecma-262</a> (2023)</li><li>. Cascading Style Sheets (CSS) Available from: <a target="_blank" href="https://www.w3.org/Style/CSS/Overview.en.html">https://www.w3.org/Style/CSS/Overview.en.html</a> (2023)</li></ol>

The authors declare no conflicts of interest.

Multiplexed ion beam imaging (MIBI) is a powerful technique for dissecting detailed cellular phenotypes and tissue histoarchitecture5,6,7,8,9,10,11. Computational efforts around MIBI have largely focused on processing the data after imaging, but little has been done to improve the instrument...

Multiplexed ion beam imaging (MIBI) is a technique to image 40+ proteins simultaneously on histologic tissue sections at up to 250 nm resolution1,2,3. After a histologic tissue section is stained using antibodies tagged with isotopically pure elemental metals, the MIBI instrument performs secondary ion mass spectrometry to simultaneously quantify all the isotopes - and thus expression of all 40+ antigens - at individual spots on the tissue. Performed across grids of millions of spots, the resulting 40+ plex images of protein expression enable the delineation of cell boundaries and identification of specific cell types while preserving spatial context1,2,3,4. This technique has been used by hundreds of users at roughly 20 sites to study the cellular composition, metabolic profiles, and/or architecture of dozens of tissue types as part of examining the immune response to tumors, tissue inflammation caused by infectious agents, neuropathology of dementia, and immune tolerance in pregnancy5,6,7,8,9,10,11.
A key bottleneck in MIBI instrument operation is setting up fields of view (FOVs) - 200&#160;x 200 &#181;m2 to 800&#160;x 800 &#181;m2 areas of the tissue - for imaging. The MIBI images one FOV at a time, up to 800&#160;x 800 &#181;m2, thus imaging larger areas requires stitching multiple FOVs together. Imaging a tissue microarray (e.g., eight circular tissues in Figure 1A) involves placing multiple FOVs spaced apart. To set up FOVs, the manufacturer interface provides 1) an optical camera image of the slide with a crosshair that roughly corresponds to the specified imaging coordinate (Figure 1A) and 2) a secondary electron detector (SED) image that shows the exact area at the coordinate, reportedly accurate to within 0.1 &#181;m (Figure 1B). First, the user roughly positions a single FOV using the optical image. Because the image resolution is only about 60 &#181;m per pixel, if the placement is off by two pixels (2 pixels&#160;x 60 &#181;m per pixel), a standard 400 &#181;m FOV will be off by 30%. Thus, the user must use the SED image to fine-tune the position - a tedious sequence of a dozen steps involving multiple popup windows, typing coordinates into text boxes, slowly nudging the SED with directional control buttons, and often even writing down coordinates on paper (Supplementary Figure 1). This process must be repeated for each spot of a 100+ core tissue microarray (TMA). Some third-party tools can help with the initial rough positioning12. However, they still require some programming knowledge, and final positioning is still done through the dozen-step process. It is also highly troublesome to position grids of adjacent FOVs, which will be later stitched together into a tiled panoramic image.
Thus, the tile/SED/array Interface (TSAI) was developed with the goal of enabling users to rapidly position large numbers of FOVs using an intuitive, interactive graphical interface. TSAI consists of two main components: 1) A web-based graphical user interface (web UI) for rapidly placing TMA points and tissue tiles, and 2) Integrations into the MIBI user control interface for generating a tiled SED image and adjusting FOV positions. If only using the optical image, many FOVs can be roughly positioned and then quickly adjusted using the FOV navigation/adjustment tools (Figure 2, TSAI, left branch). However, if the SED tiling is performed, FOVs can be accurately positioned on the tiled SED image without needing further adjustments in SED mode (Figure 2, TSAI, right branch). Of general interest to hundreds of current MIBI users, these tools make tiling and TMA positioning very simple even for novices and reduce complex MIBI run setups from several hours to a few dozen minutes.

<table><tbody><tr><td>MIBI computer</td><td>Ionpath</td><td></td><td></td></tr><tr><td>MIBIcontrol (software)</td><td>Ionpath</td><td></td><td></td></tr><tr><td>MIBIscope</td><td>Ionpath</td><td></td><td>Multiplexed Ion Beam Imaging (MIBI) microscope</td></tr><tr><td>MIBIslide</td><td>Ionpath</td><td>567001</td><td>Conductive slide for MIBI</td></tr><tr><td>Tile/SED/Array Interface (TSAI) (software)</td><td></td><td>https://github.com/ag-tsai/mibi_tsai/</td><td></td></tr></tbody></table>

rapid setup of tissue microarray and tiled area imaging on the multiplexed ion beam imaging microscope using the tile/sed/array interface

Multiplexed ion beam imaging (MIBI) is a next-generation mass spectrometry-based microscopy technique&#160;that generates 40+ plex images of protein expression in histologic tissues, enabling detailed dissection of cellular phenotypes and histoarchitectural organization. A key bottleneck in&#160;operation occurs when users select the physical locations on the tissue for imaging. As the scale and complexity of MIBI experiments have increased, the manufacturer-provided interface and third-party tools have become increasingly unwieldy for imaging large tissue microarrays and tiled tissue areas. Thus, a web-based, interactive, what-you-see-is-what-you-get (WYSIWYG) graphical interface layer - the tile/SED/array Interface (TSAI) - was developed for users to set imaging locations using familiar and intuitive mouse gestures such as drag-and-drop, click-and-drag, and polygon drawing. Written according to web standards already built into modern web browsers, it requires no installation of external programs, extensions, or compilers. Of interest to the hundreds of current MIBI users, this interface dramatically simplifies and accelerates the setup of large, complex MIBI runs.

TSAI provides two methods for setting up FOVs (Figure 2). One uses only the optical image (Figure 2, TSAI, left branch), similar to other existing methods. The second method - generating a tiled SED image - is unique to TSAI (Figure 2, TSAI, right branch). TSAI draws FOVs accurately onto this image, eliminating the need to spend hours nudging FOVs into place in the manufacturer interface SED mode. However, the correction coefficient...

Read this Scientific Article Protocol about Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope using the Tile/SED/Array Interface at JoVE.com

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Multiplexed ion beam imaging (MIBI) is often used to image tissue microarrays and tiled, contiguous tissue areas, but current software for setting up these experiments is cumbersome. The tile/SED/array interface is an intuitive, interactive graphical tool developed to dramatically simplify and accelerate MIBI run setup.

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Multiplexed ion beam imaging (MIBI) is a next-generation mass spectrometry-based microscopy technique&#160;that generates 40+ plex images of protein ...

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Research

JoVE Journal

Biology

395 Views.  Stanford University. Multiplexed ion beam imaging, of MIBI, is een techniek om eiwitexpressie in histologische weefsels over tientallen eiwitten tegelijk in beeld te brengen. Met behulp van MIBI kunnen onderzoekers cellen in hun oorspronkelijke weefselomgeving identificeren, lokaliseren en karakteriseren. Een belangrijk knelpunt bij de werking van MIBI-instrumenten is het instellen van gezichtsvelden, of FOV's, voor beeldvorming.FOV's zijn gebieden van 200 bij 200 micron t...

Video: Author Spotlight: Introductie van de Tile/SED/Array-interface voor snelle positionering van het gezichtsveld in weefselbeeldvorming

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

Bioengineering

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Targeting specific cells at ultrastructural resolution within a mixed cell population or a tissue can be achieved by hierarchical imaging using a combination of light and electron microscopy. Samples embedded in resin are sectioned into arrays consisting of ribbons of hundreds of ultrathin sections and deposited on pieces of silicon wafer or conductively coated coverslips. Arrays are imaged at low resolution using a digital consumer like smartphone camera or light microscope (LM) for a rapid large area overview, or a wide field fluorescence microscope (fluorescence light microscopy (FLM)) after labeling with fluorophores. After post-staining with heavy metals, arrays are imaged in a scanning electron microscope (SEM). Selection of targets is possible from 3D reconstructions generated by FLM or from 3D reconstructions made from the SEM image stacks at intermediate resolution if no fluorescent markers are available. For ultrastructural analysis, selected targets are finally recorded in the SEM at high-resolution (a few nanometer image pixels). A ribbon-handling tool that can be retrofitted to any ultramicrotome is demonstrated. It helps with array production and substrate removal from the sectioning knife boat. A software platform that allows automated imaging of arrays in the SEM is discussed. Compared to other methods generating large volume EM data, such as serial block-face SEM (SBF-SEM) or focused ion beam SEM (FIB-SEM), this approach has two major advantages: (1) The resin-embedded sample is conserved, albeit in a sliced-up version. It can be stained in different ways and imaged with different resolutions. (2) As the sections can be post-stained, it is not necessary to use samples strongly block-stained with heavy metals to introduce contrast for SEM imaging or render the tissue blocks conductive. This makes the method applicable to a wide variety of materials and biological questions. Particularly prefixed materials e.g., from biopsy banks and pathology labs, can directly be embedded and reconstructed in 3D.

This protocol targets specific cells in tissue for imaging at nanoscale resolution using a scanning electron microscope (SEM). Large numbers of serial sections from resin-embedded biological material are first imaged in a light microscope to identify the target and then in a hierarchical manner in the SEM.

Targeting specific cells at ultrastructural resolution within a mixed cell population or a tissue can be achieved by hierarchical imaging using a ...

Developmental Biology

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

Cancer Research

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Filamentous-actin plays a crucial role in a majority of cell processes including motility and, in immune cells, the formation of a key cell-cell interaction known as the immunological synapse. F-actin is also speculated to play a role in regulating molecular distributions at the membrane of cells including sub-membranous vesicle dynamics and protein clustering. While standard light microscope techniques allow generalized and diffraction-limited observations to be made, many cellular and molecular events including clustering and molecular flow occur in populations at length-scales far below the resolving power of standard light microscopy. By combining total internal reflection fluorescence with the super resolution imaging method structured illumination microscopy, the two-dimensional molecular flow of F-actin at the immune synapse of T cells was recorded. Spatio-temporal image correlation spectroscopy (STICS) was then applied, which generates quantifiable results in the form of velocity histograms and vector maps representing flow directionality and magnitude. This protocol describes the combination of super-resolution imaging and STICS techniques to generate flow vectors at sub-diffraction levels of detail. This technique was used to confirm an actin flow that is symmetrically retrograde and centripetal throughout the periphery of T cells upon synapse formation.

To investigate flow velocities and directionality of filamentous-actin at the T cell immunological synapse, live-cell super-resolution imaging is combined with total internal reflection fluorescence and quantified with spatio-temporal image correlation spectroscopy.

Filamentous-actin plays a crucial role in a majority of cell processes including motility and, in immune cells, the formation of a key cell-cell ...

Immunology and Infection

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly controlled, high-throughput engineered system for measuring the impact of arrayed biochemical signals on cell differentiation. Recent efforts using cell microarrays have demonstrated their utility for combinatorial studies in which many microenvironmental factors are presented in parallel. However, these efforts have focused primarily on investigating the effects of biochemical cues on cell responses. Here, we present a cell microarray platform with tunable material properties for evaluating both cell differentiation by immunofluorescence and biomechanical cell&#8211;substrate interactions by traction force microscopy. To do so, we have developed two different formats utilizing polyacrylamide hydrogels of varying Young&#39;s modulus fabricated on either microscope slides or glass-bottom Petri dishes. We provide best practices and troubleshooting for the fabrication of microarrays on these hydrogel substrates, the subsequent cell culture on microarrays, and the acquisition of data. This platform is well-suited for use in investigations of biological processes for which both biochemical (e.g., extracellular matrix composition) and biophysical (e.g., substrate stiffness) cues may play significant, intersecting roles.

Cell differentiation is regulated by a host of microenvironmental factors, including both matrix composition and substrate material properties. We describe here a technique utilizing cell microarrays in conjunction with traction force microscopy to evaluate both cell differentiation and biomechanical cell&#8211;substrate interactions as a function of microenvironmental context.

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly ...

Assembly and Tracking of Microbial Community Development within a Microwell Array Platform

The development of microbial communities depends on a combination of complex deterministic and stochastic factors that can dramatically alter the spatial distribution and activities of community members. We have developed a microwell array platform that can be used to rapidly assemble and track thousands of bacterial communities in parallel. This protocol highlights the utility of the platform and describes its use for optically monitoring the development of simple, two-member communities within an ensemble of arrays within the platform. This demonstration uses two mutants of Pseudomonas aeruginosa, part of a series of mutants developed to study Type VI secretion pathogenicity. Chromosomal inserts of either mCherry or GFP genes facilitate the constitutive expression of fluorescent proteins with distinct emission wavelengths that can be used to monitor community member abundance and location within each microwell. This protocol describes a detailed method for assembling mixtures of bacteria into the wells of the array and using time-lapse fluorescence imaging and quantitative image analysis to measure the relative growth of each member population over time. The seeding and assembly of the microwell platform, the imaging procedures necessary for the quantitative analysis of microbial communities within the array, and the methods that can be used to reveal interactions between microbial species area all discussed.

The development of microbial communities depends on a combination of factors, including environmental architecture, member abundance, traits, and interactions. This protocol describes a synthetic, microfabricated environment for the simultaneous tracking of thousands of communities contained in femtoliter wells, where key factors such as niche size and confinement can be approximated.

The development of microbial communities depends on a combination of complex deterministic and stochastic factors that can dramatically alter the spatial ...

A Guide to Build a Highly Inclined Swept Tile Microscope for Extended Field-of-view Single-molecule Imaging

Single-molecule imaging has greatly advanced our understanding of molecular mechanisms in biological studies. However, it has been challenging to obtain large field-of-view, high-contrast images in thick cells and tissues. Here, we introduce highly inclined swept tile (HIST) microscopy that overcomes this problem. A pair of cylindrical lenses was implemented to generate an elongated excitation beam that was scanned over a large imaging area via a fast galvo mirror. A 4f configuration was used to position optical components. A scientific complementary metal-oxide semiconductor camera detected the fluorescence signal and blocked the out-of-focus background with a dynamic confocal slit synchronized with the beam sweeping. We present a step-by-step instruction on building the HIST microscope with all basic components.

A detailed instruction is described on how to build a highly inclined swept tile (HIST) microscope and its usage for single-molecule imaging.

Single-molecule imaging has greatly advanced our understanding of molecular mechanisms in biological studies. However, it has been challenging to obtain ...

Revealing Murine Pulmonary Veins Myocardial Architecture

Microdissection and Immunofluorescence Staining of Myocardial Sleeves in Murine Pulmonary Veins

Pulmonary veins (PVs) are the major source of ectopic beats in atrial arrhythmias and play a crucial role in the development and progression of atrial fibrillation (AF). PVs contain myocardial sleeves (MS) composed of cardiomyocytes. MS are implicated in the initiation and maintenance of AF, as they preserve similarities to the cardiac working myocardium, including the ability to generate ectopic electrical impulses. Rodents are widely used and may represent excellent animal models to study the pulmonary vein myocardium since cardiomyocytes are widely present all over the vessel wall. However, precise microdissection and preparation of murine PVs is challenging due to the small organ size and intricate anatomy.
We demonstrate a microscopy-guided microdissection protocol for isolating the murine left atrium (LA) together with the PVs. Immunofluorescence staining using cardiac Troponin-T (cTNT) and connexin 43 (Cx43) antibodies is performed to visualize the LA and PVs in full length. Imaging at 10x and 40x magnification provides a comprehensive view of the PV structure as well as detailed insights into the myocardial architecture, particularly highlighting the presence of connexin 43 within the MS.

Author Spotlight: Developing a Translational Model for Atrial Fibrillation Research Across Species 

This protocol demonstrates microscopy-guided isolation and immunofluorescence staining of murine pulmonary veins. We prepare tissue samples containing the left atrium, pulmonary veins, and the corresponding lungs and stain them for cardiac Troponin T and Connexin 43.

Pulmonary veins (PVs) are the major source of ectopic beats in atrial arrhythmias and play a crucial role in the development and progression of atrial ...

Medicine

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Electron microscopy is applied in biology and medicine for imaging of cellular and structural details at nanometer resolution. Historically, Transmission Electron Microscopy (TEM) provided insight into cell ultrastructure, but in the recent decade, the development of modern Scanning Electron Microscopes (SEM) has changed the way of looking inside the cells. Even though the resolution of TEM is superior when protein-level structural details are needed, SEM-resolution is sufficient for the majority of the organelle-level cell biology-related questions. The advancement in technology enabled automatic volume acquisition solutions such as Serial block-face imaging (SBF-SEM) and Focused ion beam SEM (FIB-SEM). Nevertheless, to this day, these methods remain inefficient when the identification and navigation to areas of interest are crucial. Without the means for precise localization of target areas before imaging, operators need to acquire much more data than they need (in SBF-SEM), or, even worse, prepare many grids and image them all (in TEM). We propose the strategy of "lateral screening" using Array Tomography in SEM, which facilitates the localization of areas of interest, followed by automated imaging of the relevant fraction of the total sample volume. Array tomography samples are conserved during imaging, and they can be arranged into section libraries ready for repeated imaging. Several examples are shown in which lateral screening enables us to analyze structural details that are incredibly challenging to access with any other method.

We describe the preparation of ribbons of serial sections and their collection on large transfer support for use as Array Tomography samples, along with automated imaging procedures in a scanning electron microscope. The protocol allows screening, retrieval, and targeted imaging of local, rare events, and the acquisition of large data volumes.

Electron microscopy is applied in biology and medicine for imaging of cellular and structural details at nanometer resolution. Historically, Transmission ...

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays (LISCA) is a versatile method to collect time courses of fluorescence signals from individual cells in high throughput. In general, the acquisition of single-cell time courses from cultured cells is hampered by cell motility and diversity of cell shapes. Adhesive micro-arrays standardize single-cell conditions and facilitate image analysis. LISCA combines single-cell microarrays with scanning time-lapse microscopy and automated image processing. Here, we describe the experimental steps of taking single-cell fluorescence time courses in a LISCA format. We transfect cells adherent to a micropatterned array using mRNA encoding for enhanced green fluorescent protein (eGFP) and monitor the eGFP expression kinetics of hundreds of cells in parallel via scanning time-lapse microscopy. The image data stacks are automatically processed by newly developed software that integrates fluorescence intensity over selected cell contours to generate single-cell fluorescence time courses. We demonstrate that eGFP expression time courses after mRNA transfection are well described by a simple kinetic translation model that reveals expression and degradation rates of mRNA. Further applications of LISCA for event time correlations of multiple markers in the context of signaling apoptosis are discussed.

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

We present a method for the acquisition of fluorescence reporter time courses from single cells using micropatterned arrays. The protocol describes the preparation of single-cell arrays, the setup and operation of live-cell scanning time-lapse microscopy and an open-source image analysis tool for automated preselection, visual control and tracking of cell-integrated fluorescence time courses per adhesion site.

Live-cell Imaging of Single-Cell Arrays (LISCA) is a versatile method to collect time courses of fluorescence signals from individual cells in high ...

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

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