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5 minutes

This study was supported by the National Natural Science Foundation of China(U24A20790), the Ministry of Science and Technology (2022YFF0712500 and 2023YFF0722600), and the Jiangsu Provincial Department of Science and Technology(BK20250540).

1. Macrophage culture 
CAUTION: Perform all procedures in a biosafety cabinet while wearing laboratory gloves and a lab coat.
<ol>
	<li>Prepare complete culture medium (high-glucose Dulbecco&#8217;s modified Eagle medium (DMEM) supplemented with 10% foetal bovine serum (FBS) and 1% penicillin&#8211;streptomycin).</li>
	<li>Thaw a vial of RAW264.7 macrophage cells in a 37 &#176;C water bath for less than 1 min.</li>
	<li>Transfer the cell suspension to high-glucose DMEM supplemented with 10% FBS and 1% penicillin-streptomycin.</li>
	<li>Centrifuge at 300 &#215; g for 5 min.</li>
	<li>Discard the supernatant.</li>
	<li>Resuspend the cells in fresh complete medium.</li>
	<li>Seed cells into 60 mm dishes at 2&#8211;5 &#215; 105 cells per dish.</li>
	<li>Incubate cells at 37 &#176;C with 5% CO&#8322;.</li>
	<li>Do not change the medium within 12&#8211;24 h after thawing.</li>
	<li>Replace the medium every 2&#8211;3 days.</li>
	<li>Monitor cell morphology using an inverted microscope.</li>
	<li>Passage cells at 80&#8211;90% confluence by gentle pipetting. 
	NOTE: Avoid using trypsin. Use cells at passages 6&#8211;18. Select cells with uniform morphology and no vacuolization for experiments. The cell line was authenticated by STR profiling and subjected to quality control testing. Detailed reports and an English summary are provided in Supplementary Files.</li>
</ol>
2. Cell treatment
<ol>
	<li>Collect cells in the logarithmic growth phase by gentle pipetting.</li>
	<li>Resuspend cells in complete medium.</li>
	<li>Measure cell density using a cell counter.</li>
	<li>Adjust the cell density to 4 &#215; 104 cells/mL.</li>
	<li>Seed 2 mL of cell suspension into each 35 mm dish.</li>
	<li>Assign dishes to two groups: (i) LPS group, (ii) BAI + LPS group.</li>
	<li>Incubate cells at 37 &#176;C with 5% CO&#8322; for 12&#8211;16 h.</li>
	<li>Prepare a 50 mM BAI stock solution in DMSO. Store the BAI stock solution at &#8722;20 &#176;C.</li>
	<li>Add BAI to the BAI + LPS group to a final concentration of 100 &#181;M. Dilute the BAI stock solution directly in culture medium to achieve the final working concentration.</li>
	<li>Add an equal volume of DMSO to the LPS group.</li>
	<li>Prepare LPS stock solution (1 mg/mL) in complete medium.</li>
	<li>Add LPS to both groups to a final concentration of 1 &#181;g/mL. Dilute the LPS stock solution directly in culture medium to achieve the final working concentration.</li>
	<li>Proceed immediately to imaging and do not change the medium during imaging.</li>
</ol>
3. Optical diffraction tomography acquisition
<ol>
	<li>Turn on the ODT imaging system and start the environmental control module.</li>
	<li>Set the stage temperature to 37 &#176;C.</li>
	<li>Set the CO&#8322; concentration to 5%.</li>
	<li>Allow the system to equilibrate for at least 20 min.</li>
	<li>Open the acquisition software.</li>
	<li>Place the culture dish on the imaging stage.</li>
	<li>Use a 60x objective (NA = 1.42) to locate cells.</li>
	<li>Select isolated cells with clear boundaries for imaging.</li>
	<li>Set the imaging interval to 8 min.</li>
	<li>Set the total number of frames to 181.</li>
	<li>Enable the autofocus function to maintain focus during time-lapse imaging.</li>
	<li>Start time-lapse acquisition.</li>
	<li>Monitor system stability during acquisition. 
	NOTE: Autofocus stabilization was applied throughout the experiment to minimize focal drift.</li>
</ol>
4. Image reconstruction and quantitative analysis
<ol>
	<li>After image acquisition, the software automatically performs image reconstruction.</li>
	<li>Using the built-in standard algorithm.&#160;</li>
	<li>Wait until the progress bar is complete.</li>
	<li>Open the analysis software (Intellyseg, v2.1.0).</li>
	<li>Import the raw dataset by selecting Import Raw Data.</li>
	<li>Browsing to the target folder.</li>
	<li>Choose the target file and import the dataset.&#160;</li>
	<li>Click &#8220;Cell&#8221; to perform automatic segmentation using consistent settings across datasets13.</li>
	<li>Select cells based on the following criteria: (i) no overlap with neighboring cells, (ii) not in contact with image boundaries, (iii) continuously trackable throughout the time series.</li>
	<li>Select a fixed number of cells (n = 8) per field of view for consistent analysis across datasets.</li>
	<li>Open the &#8220;Long-term Tracking&#8221; module.</li>
	<li>Enter the start frame and end frame.</li>
	<li>Click &#8220;Track&#8221; to initiate tracking, which is based on cell position consistency between consecutive frames14.</li>
	<li>Manually correct tracking errors if necessary to ensure trajectory continuity: (i) Right-click incorrect masks and select &#8220;Delete Item&#8221;, (ii) Open &#8220;Manual Segmentation&#8221;, (iii) Select &#8220;Cell&#8221; and click &#8220;Add&#8221;, (iv) Use &#8220;Click&#8221; or &#8220;Box&#8221; to add correct masks.</li>
	<li>Click &#34;Export&#34;, then select &#34;Data Export&#34;.</li>
	<li>Select &#8220;Video Export&#8221; and choose output elements.</li>
	<li>Enter the start and end frames.</li>
	<li>Select the save path and file format.</li>
	<li>Click &#8220;OK&#8221; to export the video.</li>
	<li>Click &#34;Export&#34;, then select &#34;Chart System&#34;.</li>
	<li>Select &#8220;Track Chart&#8221;.</li>
	<li>Choose &#8220;Cell&#8221; as the segmentation category.</li>
	<li>Enter the tracking frame range.</li>
	<li>Select output parameters (e.g., Area, Perimeter).</li>
	<li>Click &#8220;Export Image or Excel&#8221; and save the file.</li>
	<li>Calculate the average migration speed (&#181;m/h) for each cell based on centroid displacement across the entire time series.</li>
</ol>
5. Data presentation
<ol>
	<li>Quantitative measurements are presented descriptively. No statistical inference was performed. Each treatment group included five independent samples. 
	An overview of the experimental workflow is shown in Figure 1. Key reagents, instruments, and software are listed in the Table of Materials. Imaging conditions are summarized in Table 1.</li>
</ol>

<ol>
	<li>Murray, P. J. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophage+polarization.">Macrophage polarization.</a> Annu Rev Physiol. 79, 541-566 (2017).</li><li>Xue, J., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptome-based+network+analysis+reveals+a+spectrum+model+of+human+macrophage+activation.">Transcriptome-based network analysis reveals a spectrum model of human macrophage activation.</a> Immunity. 40 (2), 274-288 (2014).</li><li>Italiani, P., Boraschi, D. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+monocytes+to+M1/M2+macrophages:+phenotypical+vs.+functional+differentiation.">From monocytes to M1/M2 macrophages: phenotypical vs. functional differentiation.</a> Front Immunol. 5, 514 (2014).</li><li>Locati, M., Curtale, G., Mantovani, A. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diversity,+mechanisms,+and+significance+of+macrophage+plasticity.">Diversity, mechanisms, and significance of macrophage plasticity.</a> Annu Rev Pathol. 15, 123-147 (2020).</li><li>Luo, M., Zhao, F., Cheng, H., Su, M., Wang, Y. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophage+polarization:+an+important+role+in+inflammatory+diseases.">Macrophage polarization: an important role in inflammatory diseases.</a> Front Immunol. 15, 1352946 (2024).</li><li>Cossarizza, A., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+for+the+use+of+flow+cytometry+and+cell+sorting+in+immunological+studies+(third+edition).">Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition).</a> Eur J Immunol. 51 (12), 2708-3145 (2021).</li><li>Unuvar Purcu, D., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+stimulation+time+on+the+expression+of+human+macrophage+polarization+markers.">Effect of stimulation time on the expression of human macrophage polarization markers.</a> PLoS One. 17 (3), e0265196 (2022).</li><li>Icha, J., Weber, M., Waters, J. C., Norden, C. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phototoxicity+in+live+fluorescence+microscopy,+and+how+to+avoid+it.">Phototoxicity in live fluorescence microscopy, and how to avoid it.</a> BioEssays. 39 (8), 1700003 (2017).</li><li>Laissue, P. P., Alghamdi, R. A., Tomancak, P., Reynaud, E. G., Shroff, H. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+phototoxicity+in+live+fluorescence+imaging.">Assessing phototoxicity in live fluorescence imaging.</a> Nat Methods. 14 (7), 657-661 (2017).</li><li>Park, Y., Depeursinge, C., Popescu, G. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+phase+imaging+in+biomedicine.">Quantitative phase imaging in biomedicine.</a> Nat Photonics. 12, 578-589 (2018).</li><li>Dong, D., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-resolution+fluorescence-assisted+diffraction+computational+tomography+reveals+the+three-dimensional+landscape+of+the+cellular+organelle+interactome.">Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome.</a> Light Sci Appl. 9, 11 (2020).</li><li>Kim, K., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+diffraction+tomography+techniques+for+the+study+of+cell+pathophysiology.">Optical diffraction tomography techniques for the study of cell pathophysiology.</a> J Biomed Photonics Eng. 2 (2), 020201 (2016).</li><li>Falk, T., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=U-Net:+deep+learning+for+cell+counting,+detection,+and+morphometry.">U-Net: deep learning for cell counting, detection, and morphometry.</a> Nat Methods. 16 (1), 67-70 (2019).</li><li>Jaqaman, K., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+single-particle+tracking+in+live-cell+time-lapse+sequences.">Robust single-particle tracking in live-cell time-lapse sequences.</a> Nat Methods. 5, 695-702 (2008).</li></ol>

The authors declare that Jie-jie Zhu and Yan-qing Yang are affiliated with Pellucid Optics (Nantong) Co., Ltd. The ODT imaging system used in this study is related to this company. However, this affiliation did not influence the experimental design, data acquisition, analysis, or interpretation of the results. The remaining authors declare no competing interests.

This study establishes a label-free ODT-based workflow for continuous imaging and quantitative analysis of macrophage morphodynamics at the single-cell level. The approach enables time-resolved measurement of projected area, perimeter, and average migration speed during long-term live-cell imaging. Cell seeding density is a key factor affecting segmentation and tracking performance. High density results in overlapping cells, whereas low density reduces the number of analyzable cells. Therefore, seeding conditions should ...

Macrophages play a central role in innate immune responses and tissue homeostasis, and their functional states can dynamically change in response to microenvironmental stimuli. Traditionally, macrophages have been described as adopting pro-inflammatory or anti-inflammatory/repair-associated phenotypes1,2. However, increasing evidence suggests that macrophage activation is a continuous and plastic process rather than a discrete binary state3,4. Under different stimuli, macrophages undergo gradual changes over time, accompanied by alterations in cell morphology and migratory behavior5. Therefore, time-resolved characterization of morphological and migratory changes at the single-cell level provides an important approach for studying dynamic cellular responses.
Current approaches for studying macrophage states mainly rely on immunostaining, flow cytometry, and transcriptomic or proteomic analyses6. These methods are typically based on fixed samples or discrete time points, which limits their ability to track continuous changes in the same cell under live conditions7. Fluorescence-based live-cell imaging enables dynamic observation but may be affected by phototoxicity and photobleaching during long-term experiments8,9. Optical diffraction tomography (ODT) is a label-free imaging technique that reconstructs the three-dimensional refractive index distribution of cells to provide quantitative structural information10. As it does not require exogenous labeling, ODT is suitable for long-term live-cell imaging and reduces perturbation to cellular behavior. In recent years, ODT has been applied to dynamic studies in various cell models11, with previous studies primarily focusing on imaging capability and structural characterization. In contrast, the present study establishes a standardized, label-free workflow for long-term, time-resolved single-cell analysis of macrophage morphodynamics under a defined in vitro stimulation condition.
The workflow is demonstrated using an in vitro RAW264.7 macrophage model with lipopolysaccharide (LPS) stimulation and Baicalin (BAI) pretreatment. BAI was used as a representative experimental modulator to establish a defined condition for demonstrating the workflow. Continuous imaging enables the extraction of quantitative single-cell parameters, including projected area, perimeter, and average migration speed, allowing time-resolved characterization of morphological and behavioral changes. This approach is intended to provide a descriptive analysis of dynamic phenotypic changes rather than to define functional states or polarization, and may serve as a complementary method to endpoint-based assays by providing temporal information. The workflow is validated in this model system and is potentially applicable to other adherent cell types.

<table><tbody><tr><td>ODT imaging system</td><td>Pellucid Optics</td><td>MH-PanoView</td><td>Used for label-free live-cell imaging</td></tr><tr><td>Acquisition software</td><td>Pellucid Optics&amp;nbsp;</td><td>Lucida-Pano v2.1.0</td><td>Used for image acquisition</td></tr><tr><td>Analysis software</td><td>Pellucid Optics&amp;nbsp;</td><td>Intellyseg v2.1.0</td><td>Used for image reconstruction, segmentation, and tracking</td></tr><tr><td>RAW264.7</td><td>Wuhan Zishan Biotechnology&amp;nbsp;</td><td>STCC20020P</td><td>Murine macrophage cell line used for in vitro polarization and imaging experiments.</td></tr><tr><td>Baicalin (BAI)</td><td>Shanghai Yuanye&amp;nbsp;</td><td>JB246114</td><td>Used for cell pretreatment</td></tr><tr><td>Lipopolysaccharide (LPS)</td><td>Solarbio</td><td>L8880</td><td>Used to stimulate macrophages</td></tr><tr><td>DMEM medium</td><td>Gibco</td><td>C11995500BT</td><td>Cell culture medium</td></tr><tr><td>Fetal bovine serum</td><td>Procell</td><td>164210</td><td>Supplement for cell culture</td></tr><tr><td>Penicillin&amp;ndash;Streptomycin (100x)</td><td>Procell</td><td>PB180120</td><td>Antibiotics for cell culture</td></tr><tr><td>60 mm culture dish</td><td>Corning</td><td>CLS430166</td><td>Used for cell culture</td></tr><tr><td>35 mm glass-bottom dish</td><td>Cellvis</td><td>D35-10-1.5-N</td><td>Used for ODT imaging</td></tr><tr><td>Inverted microscope</td><td>OLYMPUS</td><td>CKX53</td><td>Used for routine cell observation and monitoring during culture.</td></tr><tr><td>Cell counter</td><td>RWD Life Science</td><td>C100</td><td>Used for cell density determination</td></tr></tbody></table>

real-time label-free imaging and quantitative analysis of macrophage morphodynamics using optical diffraction tomography

Real-Time Label-Free Imaging and Quantitative Analysis of Macrophage Morphodynamics Using Optical Diffraction Tomography

This study establishes a label-free optical diffraction tomography (ODT)-based workflow for live-cell imaging and analysis to observe time-dependent morphological changes in macrophages. The method enables continuous recording of single-cell morphology and movement over extended periods under stable environmental conditions and allows extraction of quantitative parameters, including projected area, perimeter, and average migration speed. This workflow provides a practical approach for capturing dynamic cellular behaviors at the single-cell level without exogenous labeling. Using RAW264.7 macrophages as a model, time-lapse imaging was performed under lipopolysaccharide stimulation with baicalin pretreatment to capture dynamic cellular changes under different conditions. Representative cells were selected for tracking and quantitative analysis. The results show that this workflow supports stable long-term single-cell tracking and reflects temporal changes in cell morphology and motility. This approach provides a label-free method for observing dynamic cellular behaviors in response to different stimuli and can serve as a useful complement to conventional endpoint-based assays. It may also be applicable to other adherent cell types for studies of cell morphodynamics.

To evaluate the performance of the ODT-based workflow, time-lapse imaging was performed for two treatment groups (LPS and BAI + LPS), with five independent samples per group. All samples were acquired under identical imaging conditions. Due to the high workload associated with long-term single-cell tracking and quantitative analysis, one representative sample from each group was selected for single-cell analysis and visualization. The results are presented descriptively and are not intended for statistical inference....

This protocol describes a label-free optical diffraction tomography-based workflow for real-time imaging and quantitative analysis of macrophage morphodynamics. The method enables continuous tracking of single-cell morphology and migration, providing time-resolved measurements of projected area, perimeter, and average migration speed during long-term live-cell imaging.

This study establishes a label-free optical diffraction tomography (ODT)-based workflow for live-cell imaging and analysis to observe time-dependent ...

real-time-label-free-imaging-quantitative-analysis-macrophage

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Chengdu University of Traditional Chinese Medicine.  This protocol describes a label-free optical diffraction tomography-based workflow for real-time imaging and quantitative analysis of macrophage morphodynamics. The method enables continuous tracking of single-cell morphology and migration, providing time-resolved measurements of projected area, perimeter, and average migration speed during long-term live-cell imaging.

Real-Time Label-Free Imaging and Quantitative Analysis of Macrophage Morphodynamics Using Optical Diffraction Tomography

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