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The authors thank the support provided by the Chengdu University of Traditional Chinese Medicine School Foundation. The authors thank the support from the MPRC2022034 grant. This funding is used to develop a 3D dynamic imaging method for intestinal bacteria in Erchen Decoction to treat simple obese mice. The authors acknowledge the research platform provided by the Chengdu University of Traditional Chinese Medicine Innovative Institute of Chinese Medicine and Pharmacy and the Institute of Interdisciplinary Studies.

This protocol uses a commercial cell line (HT22, ATCC) and does not involve human subjects or primary human tissues. No animals were used in this study.
1. Standardized sample preparation
<ol>
	<li>Prepare HT22 cells
	<ol>
		<li>When the confluence of HT22 cells reaches 80%10,11, discard the old medium. Use 0.25% trypsin to dislodge the cells, incubating for 1&#8211;2 min at room temperature.</li>
		<li>Add Dulbecco&#8217;s Modified Eagle Medium (DMEM) at 3x the volume of 0.25% trypsin to stop the dislodgment. Transfer the cell suspension to a 15 mL centrifuge tube.</li>
		<li>Centrifuge at 175 x g for 3 min at room temperature, then discard the supernatant. Resuspend the cells in complete DMEM to create a single-cell suspension.</li>
		<li>Count the cells using a cell counting chamber. Seed 5,000 cells per well in 100 &#181;L of complete medium into a 96-well plate12.</li>
		<li>Incubate the plate at 37 &#176;C with 5% CO2 for 24 h. Perform sample grouping, dosing, and staining operations in the 96-well plate according to the experimental design. 
		&#8203;NOTE: Use a 96-well plate compatible with the imaging system. Select a plate with the appropriate bottom thickness and geometry, per the manufacturer&#39;s specifications. Glass-bottom plates (130&#8211;170 &#181;m bottom thickness) are recommended when higher image quality is required13,14,15,16.</li>
	</ol>
	</li>
	<li>Staining procedure
	<ol>
		<li>Ensure that cells form a single, well-dispersed monolayer and do not exceed 70% confluency to minimize cell overlap (Figure 1B).</li>
		<li>Group the samples according to the experimental requirements and proceed with staining after treatment. Prepare the working dye solutions with a final concentration of 6.684 &#181;g/mL for PI and 10 &#181;g/mL for Hoechst 3334217,18.</li>
		<li>Discard the old medium from the 96-well plate and wash each well twice with 100 &#181;L of PBS. Add the dye working solution and incubate at room temperature in the dark for 30 min for staining.</li>
		<li>Wash each well twice with 100 &#181;L of PBS, then add DMEM. 
		NOTE: Different experimental designs may have variations in sample loading and staining parameters.</li>
	</ol>
	</li>
</ol>
2. Imaging specifications and parameter settings
<ol>
	<li>Imaging parameter standards 
	&#8203;NOTE: This study demonstrates only the widefield imaging workflow for the instrument after PI and Hoechst 33342 staining.
	<ol>
		<li>Power on the instrument and then start the computer. Open the analysis software provided with the device.</li>
		<li>Load the 96-well plate into the instrument. Select the field of view with the samples for imaging.</li>
		<li>In the Objective and Camera settings, set the Magnification to &#34;20XPh1SPlanFluorELWDADM&#34; (Nikon, S Plan Fluor, ELWD, 20X/0.45, &#8734;/0-2, WD 8.2-6.9, OFN22, Ph1, AMD, MRH48230), Camera binning to &#34;2&#34;, and Acquisition Mode to &#34;Widefield&#34;.</li>
		<li>In plate mode, select the corresponding parameters of the 96-well plate and adjust the plate parameters according to the product size data.</li>
		<li>Under Site Option, typically select 9 fields per well for dense imaging. In Autofocus options, select Enable laser-based focusing.</li>
		<li>Adjust the laser autofocus parameters under Autofocus, based on the average plate bottom thickness measured from at least three sample wells. Adjust bottom thickness, Bottom thickness max variation, Adjacent well max variation, Intra-well max variation, and Plate max variation accordingly.</li>
		<li>Under Wavelengths, set the Number of wavelengths to 3. For the first Illumination, select DAPI. In Shading Correction, choose &#34;Auto Correction for FL&#34; (Figure 2B).</li>
		<li>After finding the image, set the initial Exposure (ms) to 20&#8211;30. Observe the captured images and adjust the exposure continuously to achieve a moderate fluorescence intensity without overexposure, and set the bit depth to 16 (Figure 2A).</li>
		<li>Choose Laser with z-offset for focusing adjustment. If the focus is not clear, check Use Z stack, click Calculate Offset, and select an appropriate focal distance. Check Digital Confocal (info) and set the adjustment parameters for Increase Sharpness and Reduce Noise to 0.200. 
		NOTE: If the image exposure or clarity is not satisfactory, adjust the exposure and recalculate the offset parameters.</li>
		<li>For the second Illumination, select Cy3. Follow the same settings as in steps 2.1.7-2.1.9.</li>
		<li>For the third Illumination, select TL 25. Follow the same settings as in steps 2.1.7-2.1.9. 
		NOTE: Set Shading correction based on the channel, and TL 25 should choose Auto Correction for TL. In Wavelengths, the TL channel must be set for cell positioning and morphological observation.</li>
		<li>In the Run tab, name the plate according to the naming convention: plate name-cell type-experiment ID-date-objective magnification-imaging type. Confirm the imaging channel-related parameters under Description.</li>
		<li>Click Acquire Plate to start imaging. After imaging is complete, turn off the software, instrument, and computer in sequence. 
		&#8203;NOTE: Subsequently, the computer may be opened only for image export, data processing, and result analysis</li>
	</ol>
	</li>
	<li>Imaging operation standards and requirements
	<ol>
		<li>Center the field of view on the well to avoid edge effects (Figure 1A). During imaging parameter setup, use the positive control with the highest fluorescence intensity as a reference. Additionally, when focusing, choose a field of view with a single layer of cells, clean and free of debris.</li>
		<li>For the camera, control the pixel values strictly below 60000 (the maximum value is 65535) to prevent saturation, which can cause permanent loss of information. If saturated bright spots or areas appear, shorten the exposure. At the same time, do not keep the exposure time too low; otherwise, noise will dominate the image.</li>
		<li>For weak fluorescence signals, prioritize slightly increasing the exposure rather than forcibly raising the parameters to avoid irreversible damage caused by photobleaching. 
		NOTE: For bright-field imaging, these principles can be appropriately relaxed, but the core focus remains on signal integrity and sample protection.</li>
		<li>Ensure that the sample&#39;s edge contours are clearly distinguishable and make sure that the internal fine structures (such as granules, fibers, organelles) have distinct textures, not blurred, and the background scattered light is evenly distributed without local bright spots or blurred halos. Strictly avoid over-focusing and under-focusing.</li>
		<li>Ensure that there are normal, positive, negative, and experimental groups, and that each treatment group has at least 3 parallel wells. It is recommended to arrange the parallel wells vertically.</li>
	</ol>
	</li>
</ol>
3. Image processing and analysis
NOTE: This document outlines three image processing methods provided by the instrument&#8217;s companion software, all of which are suitable for the staining protocol used in this study. While these methods share the same underlying principle of recognition, they differ in specific processing techniques and types of data results. The same image processing workflow was as follows: Open the file &#8594; Load the original image with the corresponding processing method &#8594; Set the appropriate channel parameters &#8594; Generate a Mask &#8594; Set the measurement data type in Measure &#8594; Run the analysis &#8594; Observe the results &#8594; Export the data.
<ol>
	<li>Open the analysis software provided with the device &#8594; Review Plate &#8594; Select Plate.</li>
	<li>In the Date view, select Well arrangement and choose a field of view from the positive group as a reference.</li>
	<li>Under Run Analysis, select the corresponding processing program and set the image processing parameters or export the image.</li>
	<li>After setting and saving the parameters, select Run on all wells/ Run on selections/ Run on displayed site based on the parameter setting object.</li>
	<li>After the computer finishes processing the data, under the Measurements tab, select the corresponding data processing program in Analysis and choose different measurement results in Measurement to check the data.</li>
	<li>Click Open Log to start exporting the data. Set the following details in the parameter settings for the three image processing methods:
	<ol>
		<li>Image processing method one: Multi-Wavelength Cell Scoring
		<ol>
			<li>Select the Multi-Wavelength Cell Scoring program and configure the settings. Set the Number of wavelengths. Choose Standard for the Algorithm.</li>
			<li>Set the recognition parameters for all nuclei based on the PI staining results. The default W1 Source image is the DAPI channel.</li>
			<li>Set the Approximate min width to 8&#8211;10 &#181;m and the Approximate max width to 30&#8211;35 &#181;m, based on the size of the individual objects to be recognized.</li>
			<li>Set the intensity above local background to 2,000&#8211;10,000 gray levels. Use Preview to test and adjust the settings based on the recognition results.</li>
			<li>Set the W2 parameters based on Hoechst 33342 staining results. Name this channel. The W2 Source image is Cy3</li>
			<li>In the Stained area, select the region stained by this channel as Nucleus. Follow the same settings as in steps 3.6.1.3&#8211;3.6.1.4 for subsequent settings. In Configure Summary Log or Configure Data Log (Cells), select the corresponding Parameter configuration based on the desired result parameters. 
			NOTE: Typically, Configure Summary Log is used to set the required data types, while Configure Data Log (Cells) is used when special marking of individual cells is needed.</li>
			<li>Perform a Test Run to observe the recognition. If satisfied with the recognition results, save the settings.</li>
		</ol>
		</li>
		<li>Image processing method two: Live-Dead
		<ol>
			<li>Select the Live-Dead program and begin configuring the settings. Set the Wavelength 1 Parameters and Wavelength 2 Parameters sequentially.</li>
			<li>Under Wavelength 1 Parameters, set Wavelength 1 Source to DAPI. The default Algorithm is &#34;Standard.&#34;</li>
			<li>Set Stained cell type to All Cells and Stained area to Nucleus based on the staining conditions. Set Approximate min width, Approximate max width, and intensity above local background using the same settings as the Multi-Wavelength Cell Scoring method.</li>
			<li>Check Split touching objects and use Preview to test.</li>
			<li>Under Wavelength 2 Parameters, set Wavelength 2 Source to Cy3.</li>
			<li>Set Stained cell type to Dead and Stained area to Nucleus based on the specific staining. Set Approximate min width, Approximate max width, and intensity above local background using the same settings as the Multi-Wavelength Cell Scoring method.</li>
			<li>Click on Configure Summary Log and select the Parameter configuration based on the required data types. Perform a Test Run to observe the recognition. If satisfied with the recognition results, save the settings.</li>
		</ol>
		</li>
		<li>Image processing method three: developer mode CME
		<ol>
			<li>Select the Custom Module (CME) program and begin configuring the settings. Set up Image Names and channels. 
			NOTE: To facilitate subsequent parameter settings, generally set them to the channel names used during imaging.</li>
			<li>In Find Objects, open Simple Threshold to set the recognition for the entire set.</li>
			<li>Set Source to correspond with Image Name. Based on the 16-bit grayscale value range, fill in 0 at Threshold Low and 65535 at Threshold High.</li>
			<li>Name the processing result in Result, apply, and observe the marking situation. In Find Objects, open Find Blobs to generate a Mask result.</li>
			<li>Set Source to correspond with Image Name, as in Simple Threshold. Set Approximate min width, Approximate max width, and intensity above local background as per the Multi-Wavelength Cell Scoring settings.</li>
			<li>Name the processing result in Result, apply, and observe the marking result. For the Find Blobs recognition result, use the Modify Objects tool to filter, grow, shrink, and invert.</li>
			<li>Under Measure, set Modified. In Measurement Inputs, input 1 for Standard Area Value and check Create Object Overlay.</li>
			<li>In Objects to Measure, select the source of the marked objects in Mask of Objects and the source image to be measured in Image to Measure. Choose the corresponding Measurement Name based on the required processing result parameters, apply, and observe the parameter recognition result.</li>
			<li>After confirming the parameter settings, save the configuration.</li>
		</ol>
		</li>
		<li>General method: ImageJ 
		&#8203;NOTE: Analyze the imaging results using the ImageJ method for fluorescent images19 and then perform a comparative analysis with the three methods of this instrument.
		<ol>
			<li>Open the ImageJ software. Image Import: File &#8594; Open &#8594; Select the original grayscale image to be analyzed.</li>
			<li>Convert the image to 8-bit: Image &#8594; Type &#8594; 8-bit. Grayscale Value Recognition: Image &#8594; Adjust &#8594; Threshold &#8594; Default, Red &#8594; Check Dark background &#8594; Set &#8594; OK. 
			NOTE: If the red area recognition is unsatisfactory, replace Default with one of the other 16 built-in ImageJ recognition models or adjust the slider in the dialog box.</li>
			<li>Set Parameters: Analyze &#8594; Set Measurements. In the pop-up dialog box, check Area, Mean gray value, Integrated density, Limit to threshold, Display label, Redirect to None by default, and input 2 for Decimal places (0-9). Click OK.</li>
			<li>Analyze Recognition Results: Analyze &#8594; Measure. Save Results: In the pop-up results dialog box, File &#8594; Save As, and select the save location.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Image export
	<ol>
		<li>Select a high-quality positive well field of view as the reference standard. 
		NOTE: The basic quality requirements are authenticity, accurate focus, suitable exposure (16-bit), and appropriate contrast (a higher signal-to-noise ratio is better). High-quality requirements include distinct features, contrast, practicality, intuitive morphological changes, and highlighted key points.</li>
		<li>Under Run Analysis, select ExportPro in the Analysis section, and start Run Setup for Analysis. In Select output options, choose Individual channel, Color overlay, shrink image (optional), and Scale bar (optional) based on the image requirements.</li>
		<li>Select the fluorescent channel images to be exported for each field of view. In Individual Channel, select a color for each channel, such as Blue for the DAPI channel and Monochrome for the TL channel.</li>
		<li>In Color overlay, select the channels corresponding to BRIGHTFIELD, RED, BLUE, and GREEN in order (Select the color wavelength for color overlay). Set the Number to 100; observe the preview image and accept the preview result.</li>
		<li>In output, select the folder where the exported images will be stored in Export to folder. Start the run. 
		NOTE: Images from the same batch must not be exported separately; a batch of images refers to a collection of images from four core samples: negative control, positive control, basic control group, and target experimental group, all obtained under completely consistent experimental conditions.</li>
	</ol>
	</li>
	<li>Comparison of image processing methods
	<ol>
		<li>Select 9 fields of view and compare the traditional manual counting of dead and live cells with the Multi-Wavelength Cell Scoring, Live-Dead, CME, and ImageJ methods for counting live cells, dead cells, and the ratio of dead to live cells. Use GraphPad Prism version 10.1 to perform a one-way ANOVA, accompanied by Tukey&#39;s multiple comparisons test, to analyze the differences.</li>
		<li>Select 81 fields of view and compare the differences in cell counting, area, and intensity among the Multi-Wavelength Cell Scoring, Live-Dead, CME, and ImageJ methods. Use GraphPad Prism version 10.1 to perform a one-way ANOVA, accompanied by Tukey&#39;s multiple comparisons test, to analyze the differences. 
		NOTE: Cell culture and staining experiments must be conducted within a biosafety cabinet. Dispose of waste liquid, tips, and other experimental waste according to laboratory regulations. Ensure full protection throughout the experiment.</li>
	</ol>
	</li>
</ol>

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All authors declare that there is no conflict of interest. During the translation of the manuscript, Zhipu AI was used solely for translation and not for generating research data, experimental results, or core academic content. The authors assume full responsibility for the content of the manuscript.

Using the PI and Hoechst double staining method to determine cell viability, for example, the high-content instrument can quickly and in a high-throughput manner calculate cell viability within the same batch using built-in algorithm modules. This method has several advantages, including low sample volume, low cost, minimal cell damage, weak fluorescence bleaching, resistance to quenching, and the ability to capture multiple images. Additionally, the imaging time for a 96-well plate is generally only 30&#8211;45 min<sup ...

High-Content Imaging System serves as a crucial tool in modern scientific research, enabling high-speed imaging and multi-parameter assessment of injured or uninjured cells1,2,3. It plays an irreplaceable role in various disciplines. In the field of life sciences, it assists researchers in exploring the microscopic world of cells and tissues4,5. For instance, the precise detection of fluorescently labeled molecules and structures within cells allows clear visualization of cellular fine structures and dynamic changes6. In neuroscience research, it can perform three-dimensional imaging of neurons, aiding in the analysis of the complex connections and signal transduction mechanisms of neuronal networks, and providing crucial clues for understanding brain functions and the pathogenesis of neurological diseases7,8,9.
Despite the significant advantages of High-Content Imaging System technology, several issues remain with its imaging process. In the sample preparation stage, there is a high degree of subjectivity. In biological sample preparation, processes such as cell handling and staining lack relevant standards. In terms of imaging parameters, there are notable differences in exposure times and well plate selections across laboratories and high-content microscope models. In the data processing phase, there is a lack of standardization from noise reduction and contrast adjustment of raw images to the parameter identification and determination of target features. These issues directly lead to biases in microscope imaging results, causing a crisis of reproducibility.

<table><tbody><tr><td>1.5 mL Centrifuge Tube</td><td>Labigic Technology Co., Ltd.</td><td>MCT-001-150</td><td></td></tr><tr><td>1000 &amp;micro;L Pipette Tips</td><td>Labigic Technology Co., Ltd.</td><td>35423003E</td><td></td></tr><tr><td>15 mL Centrifuge Tube</td><td>Labigic Technology Co., Ltd.</td><td>CT-002-15A</td><td></td></tr><tr><td>2.5 &amp;micro;L Pipette Tips</td><td>Qingdao Haier Biomedical Co., Ltd.</td><td>B10T</td><td></td></tr><tr><td>200 &amp;micro;L Pipette Tips</td><td>Labigic Technology Co., Ltd.</td><td>T-001-200</td><td></td></tr><tr><td>50 mL Centrifuge Tube</td><td>Labigic Technology Co., Ltd.</td><td>CT-002-50A</td><td></td></tr><tr><td>96 Well Cell Culture Plate</td><td>Corning Incorporated</td><td>3599</td><td></td></tr><tr><td>Adjustable Single-Channel Pipette, 0.1&amp;ndash;2.5 &amp;mu;L</td><td>Eppendorf SE</td><td>3120000216</td><td></td></tr><tr><td>Adjustable Single-Channel Pipette, 100&amp;ndash;1000 &amp;mu;L</td><td>Eppendorf SE</td><td>3120000267</td><td></td></tr><tr><td>Adjustable Single-Channel Pipette, 20&amp;ndash;200 &amp;mu;L</td><td>Eppendorf SE</td><td>3120000259</td><td></td></tr><tr><td>Adjustable Single-Channel Pipette, 2&amp;ndash;20 &amp;mu;L</td><td>Eppendorf SE</td><td>3120000232</td><td></td></tr><tr><td>&lt;a name=&quot;RANGE!A13&quot;&gt;Cell Culture Dish&lt;/a&gt;</td><td>Corning Incorporated</td><td>430167</td><td></td></tr><tr><td>Cryogenic Vials</td><td>Labigic Technology Co., Ltd.</td><td>BS-20-ST</td><td></td></tr><tr><td>Dimethyl sulfoxide (DMSO)</td><td>Beijing Solarbio Science &amp;amp; Technology Co., Ltd.</td><td>D8371</td><td></td></tr><tr><td>Dulbecco&#039;s Modified Eagle Medium (DMEM)</td><td>Life Technologies Limited</td><td>11966-025</td><td>In this study, the complete DMEM was prepared using 10% FBS, 1% Penicillin-Streptomycin Solution, and 89% DMEM.</td></tr><tr><td>ESCO CelCulture CO&lt;sub&gt;2&lt;/sub&gt; Incubator</td><td>Esco Micro Pte. Ltd.</td><td>CCL-170B-8</td><td></td></tr><tr><td>Fetal Bovine Serum (FBS,Superfine)</td><td>Procell Life Science &amp;amp; Technology Co.,Ltd.</td><td>164210-50</td><td></td></tr><tr><td>Hoechst 33342 Fluorescent Dyes</td><td>Beijing Solarbio Science &amp;amp; Technology Co., Ltd.</td><td>B8040</td><td></td></tr><tr><td>HT22 cell</td><td>Procell Life Science &amp;amp; Technology Co.,Ltd.</td><td>CL-0697</td><td></td></tr><tr><td>ImageJ</td><td>National Institutes of Health</td><td></td><td>ImageJ is one of the image processing software used in the manuscript.</td></tr><tr><td>ImageXpress Micro Confocal High-Content Imaging System</td><td>Molecular Devices, LLC</td><td>ImageXpress Micro Confocal</td><td>In the manuscript, use &quot;High-Content Imaging System&quot; as a substitute for the instrument name.</td></tr><tr><td>Low speed freezing centrifuge</td><td>Hunan Xiangyi Laboratory Instrument Development Co., Ltd.</td><td>L530R</td><td></td></tr><tr><td>MetaXpress High-Content Image Acquisition and Analysis Software</td><td>Molecular Devices, LLC</td><td></td><td>The software is a companion to the ImageXpress&amp;reg; Micro Confocal High-Content Imaging System. In the manuscript, &quot;The analysis software provided with the device&quot; refers to this software.</td></tr><tr><td>OptiMair Vertical Laminar Flow Cabinet</td><td>Esco Micro Pte. Ltd.</td><td>ACB-4E1</td><td></td></tr><tr><td>Parafilm M</td><td>Pechiney Plastic Packaging Inc.</td><td>PM996</td><td></td></tr><tr><td>PBS</td><td>&amp;nbsp;Shanghai Yuchun biology science and technology co., ltd&amp;nbsp;</td><td>YC-5013</td><td></td></tr><tr><td>Penicillin-Streptomycin Solution</td><td>Procell Life Science &amp;amp; Technology Co.,Ltd.</td><td>PB180120</td><td></td></tr><tr><td>Propidium Iodide Solution (PI)</td><td>Beijing Solarbio Science &amp;amp; Technology Co., Ltd.</td><td>C0080</td><td></td></tr><tr><td>Trypsin-EDTA Solution</td><td>BasalMedia Technology Co.,Ltd.</td><td>S310KJ</td><td></td></tr></tbody></table>

standardized workflow for high-content imaging and data processing

Standardized Workflow for High-Content Imaging and Data Processing

To address the challenges of low reproducibility due to operational variations in scientific research imaging and the complexity of analyzing a large number of samples using conventional methods, we introduce a protocol for staining HT22 cells with PI and Hoechst 33342 and then imaging and analyzing the data using the High-Content Imaging System. This workflow aims to establish robust imaging setting parameters and stringent quality control standards. It demonstrates the instrument&#39;s imaging settings and the workflow for three different image processing methods provided by the instrument: Multi-Wavelength Cell Scoring (Multi), Live-Dead (Live), and Custom Module Editor (CME). The image processing methods are compared and validated against the traditional method, ImageJ, to ensure their effectiveness. The study ultimately summarizes a workflow for using the High-Content Imaging System to determine cell viability using the PI and Hoechst double-staining method. By standardizing the entire imaging and data processing workflow, the study significantly enhances the reproducibility and reliability of scientific research imaging, thereby providing solid methodological support for cross-laboratory data comparison and advancing scientific research.

Impact of standardized preparation on imaging quality
The plate parameters were configured to ensure that the instrument could reliably locate the cell image layer. Samples prepared as a single layer of cells with moderate confluence facilitated image focusing at different objective magnifications and improved image recognition and quantitative analysis. Standardized sample preparation and optimized plate parameters effectively minimized edge effects, cell overlap, and other f...

Here, we present a protocol for standardized high-content imaging and data processing of propidium iodide (PI)/Hoechst 33342-stained cells to perform reproducible cell viability analysis using automated image acquisition and analysis workflows.

To address the challenges of low reproducibility due to operational variations in scientific research imaging and the complexity of analyzing a large ...

standardized-workflow-for-high-content-imaging-and-data-processing

Research

JoVE Journal

Biology

A high-content screening method for the identification of novel signaling competent transmembrane receptors is described. This method is amenable to large-scale automation and allows predictions about in vivo protein binding and the sub-cellular localization of protein complexes in mammalian cells.

A High-content Imaging Workflow to Study Grb2 Signaling Complexes by Expression Cloning

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Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

High-resolution cryo-EM maps of macromolecules can be also achieved by using 200 kV TEM microscopes. This protocol shows the best practices for setting accurate optics alignments, data acquisition schemes, and selection of imaging areas that are all essential for the successful collection of high-resolution datasets using a 200 kV TEM.

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.

High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples

This article describes step-by-step methods to automate image-based nuclei quantification using an open-source executable program validated across a range of cell densities. This program provides an alternative that addresses barriers related to cost, accessibility for users with limited technological skillsets, and application-specific validation that may limit utility of existing technologies.

Using Computer Vision Libraries to Streamline Nuclei Quantification

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Optimized Workflow for Iterative Bleaching Extends Multiplexity Imaging of Highly Autofluorescent Clinical Samples

Spectral Iterative Bleaching Extends Multiplexity (IBEX) builds upon the base IBEX technique by adding heparin blocking to minimize nonspecific binding and leveraging spectral detection with computational unmixing to suppress autofluorescence. This approach accelerates image acquisition while reducing sources of background, enabling robust multi-round, high-parameter spatial proteomic analyses.

High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence

The workflow of automated digital cytomorphology of peripheral blood and bone marrow is described. After standardized smear preparation and staining, a scanner captures specimen images. AI-powered software locates, records, and preliminarily classifies nucleated cells, followed by expert review. The focus is on critical cellular misclassifications with potential for misdiagnosis.

Workflow for Automated Digital Peripheral Blood and Bone Marrow Cytomorphology, With Consideration of Critical Cellular Misclassifications

Chengdu University of Traditional Chinese Medicine.  Here, we present a protocol for standardized high-content imaging and data processing of propidium iodide (PI)/Hoechst 33342-stained cells to perform reproducible cell viability analysis using automated image acquisition and analysis workflows.

Standardized Workflow for High-Content Imaging and Data Processing

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