Name: Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging (Video) | JoVE Journal
Uploaded: 2026-01-30T13:15:00.000+00:00
Duration: 9 min 10 s
Description: Discover imaging-based chromatin and epigenetic age (ImAge) for quantifying aging at single-cell resolution. This non-destructive method enables efficient biological age estimation without sample destruction.

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5 minutes

We thank all current and former Terskikh lab members for their help with establishing the protocols and computational framework. This work was supported by National Institutes of Health grants R21 AG068913, R21 AG075483, R21 AG083782, and the Future Health Research and Innovation Fund of the WA Department of Health, BU/PG: 01760/47004300 to A.V.T.

All animal experiments are conducted according to guidelines and protocols approved by the Institutional Animal Care and Use Committee (IACUC) of The Scripps Research Institute (Protocol 09-0004-6). Data presented within this manuscript were obtained using male i4F mice, ranging in age from 2 to 14 months of age. Organs of interest (liver and skeletal muscle) were harvested from 13.8-month-old i4F and littermate control mice treated for 1 week with a low dose of doxycycline (0.2 mg/ml)14. Nuclei were isolated from samples of young (3.2 months), aged (13.8 months), and aged treated with doxycycline to overexpress OSKM factors (aged-OSKM).
1. Imaging of samples
<ol>
	<li>Sample preparation
	<ol>
		<li>Collect organs of interest (liver and skeletal muscle) from freshly dissected mice (or other species of interest) and flash freeze by liquid nitrogen. Store samples at -80 &#176;C until ready to use.</li>
		<li>Transfer organs and tissues to a pre-chilled mortar and lay over dry ice. Pour liquid nitrogen over the frozen tissue and grind using a pre-chilled pestle. Pulverize the sample using the pre-chilled mortar and pestle until a uniformly fine powder is obtained.</li>
		<li>Aliquot ground-up tissue using a pre-chilled metal spatula into multiple tubes and store unused samples at -80 &#176;C. 
		NOTE: Ground tissue samples in the tubes can be stored at -80 &#176;C or proceed with nuclei isolation.</li>
		<li>Extract nuclei from frozen aliquots, using a commercially available Nuclei Lysis Buffer kit as per the manufacturer&#39;s instructions.</li>
		<li>Count nuclei using Trypan Blue. Mix nuclei and Trypan Blue in equal ratio in a separate tube. Transfer 10 &#181;l of the nuclei-Trypan Blue solution to a hemocytometer or an automated cell counter.</li>
		<li>Dilute samples in PBS + 0.5% BSA such that 10,000 nuclei can be plated in a volume of 20-30 &#181;L/well.</li>
	</ol>
	</li>
	<li>Plating preparation
	<ol>
		<li>Enter all relevant metadata for plating preparation and computational analysis into the Platemap (provided in Supplementary File 1). Record the sample identifiers (e.g., numbers or characters) in the same layout as the actual well plate in the sampleID tab. Record experimental conditions, including the sample&#39;s age, in the same layout as the actual well plate in the condition tab. 
		NOTE: The information provided in the condition tab will be utilized to identify the age group of each sample (i.e., young or old). These identified age groups will subsequently serve as references for constructing the aging (ImAge) prediction model. Therefore, the condition tab must contain data that can be associated with age, along with other relevant experimental conditions as necessary.</li>
		<li>Record the target biomarkers (e.g., H3K27me3) in the same layout as the actual well plate in the channel# (# is replaced by a number) tab. Verify that all combinations of metadata match and are correctly reflected in one consecutive table format in the platemap tab. 
		NOTE: Each column in the table should correspond to a designated tab name (e.g., &#34;sampleID&#34;). An additional tab labeled &#34;metadata&#34; is provided for documenting project objectives and staining details; this information will not be utilized in the data analysis. Utilize this tab as deemed necessary.</li>
		<li>Export the tab platemap as csv file format with the short, descriptive project name (e.g., project.csv). 
		NOTE: The metadata tab provides a template to summarize project aims and immunostaining information. The use of the metadata tab in the Platemap template is optional for the plating process itself. However, other tabs, including sampleID, channel# (# is replaced by a number), are required for computational analysis.</li>
	</ol>
	</li>
	<li>Plate 10,000 nuclei/well in 20 - 30 &#181;L volume in a well plate (e.g., 384-well plates). Spin plate at 1,000 x g (acceleration nine, deceleration 4) for 10 min at 4 &#176;C.</li>
	<li>Fix with 1:1 volume of 8% paraformaldehyde (PFA) in PBS for 10 min at room temperature. 
	CAUTION: Conduct PFA steps in a fume hood and dispose of waste as per institutional safety procedures.</li>
	<li>Remove solution. Add 30 &#181;L of 0.1 M glycine in PBS for 5 min at room temperature. Remove solution. Wash with 100 &#181;L of PBS 2x and add 30 &#181;L of PBS for storage. 
	NOTE: Cells can be stored at 4 &#176;C or proceed immediately with immunolabeling.</li>
	<li>Immunolabeling
	<ol>
		<li>Remove PBS. Add 30 &#181;L of blocking solution (2% BSA and 0.5% Triton X-100 in PBS) and incubate for 1 h at room temperature. Remove the blocking solution.</li>
		<li>Add 25 &#181;L of primary antibodies: Mouse anti-H3K27ac (1:250) and Rabbit anti-H3K27me3 (1:250) diluted in blocking buffer (0.4 - 2 &#181;g/mL). Incubate overnight at 4 &#176;C.</li>
		<li>Remove primary antibody solution. Wash with 100 &#181;L of PBS 3x for 3 min at room temperature.</li>
		<li>Add 25 &#181;L of secondary antibodies: Alexa 488 Donkey Anti-Mouse (1:250) and Alexa 555 Donkey Anti-Rabbit (1:250) diluted in blocking buffer (1 - 10 &#181;g/mL). Incubate for 2 h at 4 &#176;C or 1 h at room temperature.</li>
		<li>Wash with 100 &#181;L of PBS 3x for 3 min at room temperature. Stain with DAPI/Hoechst in 30 &#181;L of 0.01 mg/mL concentration. Wash with 100 &#181;L of PBS. 
		NOTE: Store plate at 4 &#176;C in foil or proceed immediately with imaging. DAPI/Hoechst is required for the nuclei segmentation step.</li>
	</ol>
	</li>
	<li>Imaging
	<ol>
		<li>Image cells on a high-content imager for maximum resolution (for this data set, 20x was used, but 63x is recommended). 
		NOTE: Although imaging can be performed with any high content imager, this protocol assumes a flat folder structure of acquired images and a generic image format (e.g., .tiff) as the acquired image output (Figure 2).</li>
		<li>Collect multiple fields per well and multiple z-stacks, ideally covering from top to bottom of cell nuclei. A 10 &#181;m coverage along with the z-axis (e.g., 20 z-slices at a 0.5 &#181;m z-step) typically covers most mouse cell nuclei. The size and required coverage depend on the species and cell types of interest. Representative imaging is shown in Figure 3.</li>
		<li>Transfer the imaging data to the computer where computational analysis will be performed, following the folder structure presented in Figure 2.</li>
		<li>Add the square-bracket-annotated prefix of the project name defined in step 1.2.2 above to the image data folder (e.g., [project]original_folder_name)</li>
	</ol>
	</li>
</ol>
2. Computational analysis of imaging data
<ol>
	<li>System software requirement preparation (Required for Initial Setup Only)
	<ol>
		<li>Install Windows Subsystem for Linux (WSL) if operating on a Windows machine by following the instructions at https://learn.microsoft.com/en-us/windows/wsl/install. Install Anaconda or Miniconda by following the instructions at https://www.anaconda.com/docs/getting-started/anaconda/main&#160;</li>
	</ol>
	</li>
	<li>Preparation of computational environment (Required for Initial Setup Only)
	<ol>
		<li>Download the workflow scripts from github repository&#160;https://github.com/terskikh-lab/ImAge_workflow. Unzip the downloaded file. 
		NOTE: The data storage location may be designated arbitrarily; however, it is recommended to select a directory distinct from the system file storage location (e.g., the C drive on Windows).</li>
		<li>Organize the cloned ImAge repository to match the assumed folder structure for proper script execution (Figure 2).</li>
		<li>Open terminal app if operating on Linux or macOS. Open the WSL app if operating on Windows.</li>
		<li>Change folder to the cloned repository file. Henceforth, the term Command: denotes an instruction to copy and paste the subsequent code into the terminal application, followed by pressing the Enter key. 
		Command: cd /PATH_TO_CLONED_REPOSITORY/ImAge-main 
		NOTE: Replace &#34;/PATH_TO_CLONED_REPOSITORY&#34; with the user-specific setting. For example, when the repository is unzipped in the &#34;analysis&#34; folder of the D drive and accessed from WSL, then this will be &#34;/mnt/d/analysis&#34;.</li>
		<li>Create a new computational environment with Python version 3.10 using conda (command line application installed with Anaconda/miniconda). 
		Command: conda create -n ImAge_workflow python=3.10</li>
		<li>Activate the newly created environment. 
		&#8203;Command: conda activate ImAge_workflow</li>
		<li>Install poetry (a python package manager) using pip installer (python package installer installed when creating the ImAge_workflow environment). 
		&#8203;Command: pip install poetry</li>
		<li>Install prerequisite python packages using poetry. 
		Command: poetry install 
		&#8203;CAUTION: Make sure the current folder is the ImAge_workflow folder, which can be verified by running a command: pwd.</li>
		<li>Configure CUDA within the Conda environment to enable graphic processing unit (GPU) functionality in the segmentation step for faster computation (only applicable for linux or Windows PC with Nvidia GPU). 
		Command: mkdir -p $CONDA_PREFIX/etc/conda/activate.d 
		echo &#39;NVIDIA_PACKAGE_DIR=$(dirname $(python -c &#34;import nvidia;print(nvidia.__file__)&#34;))&#39; &#62;&#62; $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh 
		echo &#39;for dir in $NVIDIA_PACKAGE_DIR/*; do if [ -d &#34;$dir/lib&#34; ]; then export LD_LIBRARY_PATH=&#34;$dir/lib:$LD_LIBRARY_PATH&#34;; fi done&#39; &#62;&#62; $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh 
		&#8203;NOTE: The scripts automatically detect GPU availability on the server. If no GPUs are available, it will default to running on the CPU.</li>
	</ol>
	</li>
	<li>Run computations as described below. 
	NOTE: The computational analysis consists of four steps: nuclei segmentation, image feature extraction, aging (ImAge) axis construction, and prediction of ImAge. Each process is performed using python scripts; o1_illumination_correction, o2_segmentation, o3_extract_features, and o4_ImAge_validation. All these steps are included in one Python script, main.py. The extracted ImAge readouts can be visualized using a box and violin plot as presented in Figure 4. The ImAge readout can also be extracted in a straightforward table format (standard .csv file format), with samples represented as rows and corresponding readouts and metadata as columns. Users have the flexibility to perform additional numerical analyses on the ImAge readouts as needed. These plots and the CSV file can be obtained by using one Python script, visualization.py. The ImAge prediction process utilizes bootstrapped signals obtained from individual cells within the same tissue. These bootstrapped signals are averaged signals derived from random sampling and repeated iterations. This approach aims to ensure that the analysis captures the most reliable and representative readouts, with the uncertainty of the analysis proportional to the inherent heterogeneity of the tissue.
	<ol>
		<li>Open terminal app if operating on Linux or macOS. Open the WSL app if operating on Windows. 
		Command: cd /PATH_TO_CLONED_REPOSITORY/ImAge-main 
		&#8203;CAUTION: Make sure the current folder is the ImAge_workflow folder, which can be verified by running a command: pwd.</li>
		<li>Activate the environment. 
		&#8203;Command: conda activate ImAge_workflow</li>
		<li>Configure the analysis parameters on workflow/main.py file in the code or text editor (e.g., Notepad app, Microsoft Visual Studio Code, etc.).
		<ol>
			<li>Set the project name in the variable p to a short, filesystem-safe identifier used to organize outputs (e.g., p = brain_3ages_k27me3).</li>
			<li>Set the list of channels for feature extraction in chs. Include the segmentation channel if features from that channel are required for the analysis (chs = [&#39;DAPI&#39;, &#39;H3K27me3&#39;, &#39;H3K27ac&#39;]).</li>
			<li>Map image channel identifiers from filenames to plate map columns using imageIndex. Determine the channel label format used by the imaging system to acquire image data and use that label as the key. Map each label to the exact, case-sensitive column header in the platemap saved in step 1.2.2 (e.g., imageIndex = {&#39;1&#39;: &#39;Channel1&#39;, &#39;2&#39;: &#39;Channel2&#39;, &#39;3&#39;: &#39;Channel3&#39;}). 
			CAUTION: Column names are case-sensitive and must correspond to your plate map file.</li>
			<li>Set data locations: orgDataLoadPath to the dataset root, orgDataSubFolder to the image subfolder if necessary, and resultsSavePath to the output root.</li>
			<li>Confirm the filename extension in imageFileFormat and edit imageFileRegEx to use Python named capturing groups for all required parts: row, col, field, zposition, and channel. Use the following syntax: (?P&#60;GROUP&#62;\d+), where GROUP is replaced by row, col, field, zposition, or channel. 
			NOTE: Update the regex if the filenames differ; all named groups must parse correctly. For example, the file name r01c11f01p01-ch1sk1fk1fl1.tiff should be converted to the following: r(?P&#60;raw&#62;\d+)c(?P&#60;col&#62;\d+)f(?P&#60;field&#62;\d+)p(?P&#60;zposition&#62;\d+)-ch(?P&#60;channel&#62;\d+)sk1fk1fl1\.tiff.</li>
			<li>Set the segmentation channel in segmentation_ch to the nuclear channel used for object detection (e.g., segmentation_ch = &#39;DAPI&#39;).</li>
			<li>Decide whether to use illumination correction by setting illumiCorrection. Set to True to compute and apply BaSiC models; set to False to process raw images. 
			NOTE: For plates with &#8805; 25 wells, illumination correction is recommended to reduce systematic bias.</li>
			<li>Set the physical voxel size in micrometers in voxel_dim as [z, y, x]. Use microscope metadata to determine spacing (e.g., voxel_dim = [1.0, 0.6, 0.6]). Save the file and close the editor after verifying each parameter</li>
		</ol>
		</li>
		<li>Run scripts to obtain ImAge readouts and perform validation. While running the script, do not close the terminal or WSL&#160;app. 
		Command: Python workflow/main.py 
		NOTE: The script (main.py) encompasses all steps, including segmentation, feature extraction, ImAge axis construction, and prediction by default. The script will generate an estimated completion time for each step (e.g., nuclei segmentation). Each process (e.g., o1_illumination_correction) will generate interim files in .pickle format that contain results from each process. However, this is intended solely for data transfer between processes to ensure standardized processing and is not intended for user manipulation in this article. Nevertheless, if users need to manipulate or adopt these files, see the documentation included in each Python function for the data shape and types13.</li>
		<li>Visualize results and export raw ImAge readouts for additional analysis on external software.Command: Python workflow/visualization.py 
		NOTE: This step produces figures and tables of ImAge readouts, and the files are located at fig_o4_ImAge_validation_VIOLIN and export_o4_ImAge_validation, respectively (Figure 2).</li>
	</ol>
	</li>
</ol>

<ol><li>Alvarez-Kuglen, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ImAge+quantitates+aging+and+rejuvenation%5BTitle%5D)+AND+%22Nat+Aging%22%5BJournal%5D)">ImAge quantitates aging and rejuvenation.</a> Nat Aging. 4, 1308-1327 (2024).</li>
<li>Zampino, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biomarkers+of+aging+in+real+life%3A+three+questions+on+aging+and+the+comprehensive+geriatric+assessment%5BTitle%5D)+AND+%22GeroScience%22%5BJournal%5D)">Biomarkers of aging in real life: three questions on aging and the comprehensive geriatric assessment.</a> GeroScience. 44 (6), 2611-2622 (2022).</li>
<li>Moqri, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biomarkers+of+aging+for+the+identification+and+evaluation+of+longevity+interventions%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Biomarkers of aging for the identification and evaluation of longevity interventions.</a> Cell. 186 (18), 3758-3775 (2023).</li>
<li>Bell, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DNA+methylation+aging+clocks%3A+challenges+and+recommendations%5BTitle%5D)+AND+%22Genome+Biol%22%5BJournal%5D)">DNA methylation aging clocks: challenges and recommendations.</a> Genome Biol. 20 (1), 249(2019).</li>
<li>Horvath, S., Raj, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DNA+methylation-based+biomarkers+and+the+epigenetic+clock+theory+of+ageing%5BTitle%5D)+AND+%22Nat+Rev+Genet%22%5BJournal%5D)">DNA methylation-based biomarkers and the epigenetic clock theory of ageing.</a> Nat Rev Genet. 19 (6), 371-384 (2018).</li>
<li>Field, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DNA+methylation+clocks+in+aging%3A+Categories%2C+causes%2C+and+consequences%5BTitle%5D)+AND+%22Mol+Cell%22%5BJournal%5D)">DNA methylation clocks in aging: Categories, causes, and consequences.</a> Mol Cell. 71 (6), 882-895 (2018).</li>
<li>Galow, A. M., Peleg, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+to+slow+down+the+ticking+clock%3A+Age-associated+epigenetic+alterations+and+related+interventions+to+extend+life+span%5BTitle%5D)+AND+%22Cells%22%5BJournal%5D)">How to slow down the ticking clock: Age-associated epigenetic alterations and related interventions to extend life span.</a> Cells. 11 (3), 468(2022).</li>
<li>Westneat, D. F., Wright, J., Dingemanse, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+biology+hidden+inside+residual+within-individual+phenotypic+variation%5BTitle%5D)+AND+%22Biol+Rev+Camb+Philos+Soc%22%5BJournal%5D)">The biology hidden inside residual within-individual phenotypic variation.</a> Biol Rev Camb Philos Soc. 90 (3), 729-743 (2015).</li>
<li>Lähnemann, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Eleven+grand+challenges+in+single-cell+data+science%5BTitle%5D)+AND+%22Genome+Biol%22%5BJournal%5D)">Eleven grand challenges in single-cell data science.</a> Genome Biol. 21 (1), 31(2020).</li>
<li>Ninomiya, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ImAge%3A+quantifying+epigenetic+ageing+with+single-cell+images%5BTitle%5D)+AND+%22Nat+Rev+Cancer%22%5BJournal%5D)">ImAge: quantifying epigenetic ageing with single-cell images.</a> Nat Rev Cancer. 25, 755(2025).</li>
<li>Stepanov, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genetically+encoded+epigenetic+sensors+for+visualization+of+H3K9me3%2C+H3K9ac+and+H3K4me1+histone+modifications+in+living+cells%5BTitle%5D)+AND+%22Biochem+Biophys+Res+Commun%22%5BJournal%5D)">Genetically encoded epigenetic sensors for visualization of H3K9me3, H3K9ac and H3K4me1 histone modifications in living cells.</a> Biochem Biophys Res Commun. 733, 150715(2024).</li>
<li>Farhy, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Improving+drug+discovery+using+image-based+multiparametric+analysis+of+the+epigenetic+landscape%5BTitle%5D)+AND+%22Elife%22%5BJournal%5D)">Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape.</a> Elife. 8, e49683(2019).</li>
<li>ImAge_workflow. , 
 <a target="_blank" href="https://github.com/terskikh-lab/ImAge_workflow">https://github.com/terskikh-lab/ImAge_workflow</a> (2025).</li>
<li>Chondronasiou, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multi-omic+rejuvenation+of+naturally+aged+tissues+by+a+single+cycle+of+transient+reprogramming%5BTitle%5D)+AND+%22Aging+Cell%22%5BJournal%5D)">Multi-omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming.</a> Aging Cell. 21 (3), e13578(2022).</li>
<li>Linkert, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metadata+matters%3A+access+to+image+data+in+the+real+world%5BTitle%5D)+AND+%22J+Cell+Biol%22%5BJournal%5D)">Metadata matters: access to image data in the real world.</a> J Cell Biol. 189 (5), 777-782 (2010).</li>
<li>The Open Microscopy Environment. , bioformats. 
 <a target="_blank" href="https://github.com/ome/bioformats">https://github.com/ome/bioformats</a> (2025).</li>
<li>González-Ramírez, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Differential+contribution+to+gene+expression+prediction+of+histone+modifications+at+enhancers+or+promoters%5BTitle%5D)+AND+%22PLoS+Comput+Biol%22%5BJournal%5D)">Differential contribution to gene expression prediction of histone modifications at enhancers or promoters.</a> PLoS Comput Biol. 17 (9), e1009368(2021).</li>
</ol>

The authors declare no conflicts of interest.

Sample Preparation 
For sample preparation, promptly harvest tissues of interest and immediately flash-freeze them in liquid nitrogen. When grinding tissue with a mortar and pestle, a silicone cover for the mortar can be used to prevent sample loss. Add more liquid nitrogen if the tissue is difficult to pulverize. For more fragile tissues, samples can also be processed fresh (rather than frozen) to reduce mechanical disruption or tissue damage. Nuclei isolation protocols may need optimization depend...

The increasing average lifespan and an aging global population underscore the critical need for effective biomarkers of functional or biological age1,2,3. These biomarkers are essential for accurately predicting disease risk and lifespan, which can significantly enhance geriatric care and reduce associated costs. While DNA methylation (DNAm) clocks have made notable advancements in quantifying biological aging4,5,6, they often rely on linear regression and necessitate large cohorts, potentially overlooking nuanced, context-dependent biological components specific to individuals7,8. Furthermore, these sequencing-based techniques require cell lysis, complex sample preparation, and expensive reagents for each cell, making them more costly and challenging for high-throughput, large-scale, or longitudinal studies where sample destruction is a disadvantage9.
To overcome these limitations, we developed imaging-based chromatin and epigenetic age (ImAge), a novel technique designed to quantify aging and rejuvenation at single-cell resolution. This technique was applied and validated in mouse peripheral blood and various solid organs as described by Alvarez-Kuglen et al.1. This publication describes the optimized protocols in detail such that they can be adapted and adopted by other researchers for various applications.
This approach fundamentally differs from traditional DNAm clocks by capturing intrinsic age-related trajectories based on the spatial organization of chromatin and epigenetic marks within single nuclei1. Imaging-based methods offer distinct advantages: it is inherently non-destructive, meaning cells are not destroyed during processing, unlike sequencing-based methods. This non-destructive nature crucially allows for the preservation of the spatial structure of chromatin and epigenetic marks at the single-cell level, maintaining the spatial context of cells within tissues-a perspective often lost in conventional sequencing workflows. Furthermore, imaging techniques are generally more cost-effective for large-scale or longitudinal studies, as they do not require expensive reagents or complex library preparation per cell, making them suitable for repeated observations over time10,11.
A key advantage of ImAge is its ability to reveal age-related trajectories as intrinsic principal features of the data itself, without requiring regression on chronological age. ImAge has demonstrated a strong correlation with chronological age in mouse peripheral blood mononuclear cells and various solid organs1. Crucially, ImAge effectively captures expected perturbations to biological age, showing an increase following chemotherapy treatment and a decrease with caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Furthermore, ImAge readouts have been observed to inversely correlate with locomotor activity in chronologically identical mice, indicating their utility in measuring aspects of biological and functional age.
Implementing the ImAge methodology necessitates expertise in both wet-lab experimental procedures12 and computational analyses1 (Figure 1); therefore, this paper provides comprehensive, step-by-step protocols for both aspects. This optimized protocol aims to facilitate the widespread adoption of ImAge, enabling researchers to investigate age-associated changes in chromatin and epigenetic organization at single-cell resolution across various cell types and tissues. In this protocol, H3K27ac and H3K27me3, two markers that provide sufficient contrast and resolution for determining ImAge, were selected as representative marks. Researchers can replicate this protocol to quantify biological aging, assess the impact of longevity interventions, and explore the inherent heterogeneity within aging and rejuvenation processes at the individual organism level.

<table><tbody><tr><td>Bovine Serum Albumin</td><td>Sigma-Aldrich&amp;nbsp;</td><td>A9647</td><td></td></tr><tr><td>Donkey Anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa 488&amp;nbsp;</td><td>Invitrogen</td><td>A-21202</td><td></td></tr><tr><td>Donkey Anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa 555</td><td>Invitrogen</td><td>A-31572</td><td></td></tr><tr><td>Glycine</td><td>Bio-Rad</td><td>161-0718</td><td></td></tr><tr><td>Histone H3K27ac antibody (mAb)&amp;nbsp;</td><td>Active Motif</td><td>39685</td><td></td></tr><tr><td>Histone H3K27me3 antibody (pAb)&amp;nbsp;</td><td>Active Motif</td><td>39155</td><td></td></tr><tr><td>Hoechst 33342</td><td>Invitrogen</td><td>H3570</td><td></td></tr><tr><td>Nuclei Isolation Kit: Nuclei EZ Prep</td><td>Sigma-Aldrich</td><td>NUC101</td><td></td></tr><tr><td>Operetta CLS High Content Imager</td><td>Revvity</td><td>https://www.revvity.com/au-en/product/operetta-cls-system-hh16000020</td><td></td></tr><tr><td>Paraformaldehyde 16% Aqueous Solution EM Grade</td><td>Electron Microscopy Sciences</td><td>15710-S</td><td></td></tr><tr><td>PBS</td><td>Thermo Fisher</td><td>AM9624</td><td></td></tr><tr><td>PhenoPlate 384-well Microplate</td><td>Revvity</td><td>6057302</td><td></td></tr><tr><td>Triton-X</td><td>Sigma</td><td>T8787</td><td></td></tr><tr><td>Trypan Blue Solution, 0.4%&amp;nbsp;</td><td>Gibco&amp;nbsp;</td><td>15250061&amp;nbsp;</td><td></td></tr></tbody></table>

measuring single-cell aging with an imaging-based biomarker of chromatin and epigenetic aging

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

To advance personalized healthcare and assess potential rejuvenation strategies, biomarkers reflecting biological age are crucial. We have introduced imaging-based chromatin and epigenetic age (ImAge), an imaging-based biomarker derived from the spatial organization of chromatin and epigenetic marks within single nuclei, capturing intrinsic age-related changes. ImAge effectively captures and quantifies the effects of interventions modulating biological age, showing sensitivity through significant decreases following rejuvenation strategies (such as caloric restriction and partial reprogramming through transient Oct4, Sox2, Klf4, and Myc (OSKM) expression) and increases after treatments like chemotherapy. Furthermore, lower ImAge correlated with higher locomotor activity, suggesting it captures functional improvements alongside biological age reversal, offering a promising single-cell tool to evaluate longevity interventions. As ImAge is an integrative method requiring good-quality imaging and meticulous computational analysis, we aim to provide thorough guidance to extract and validate it. This comprehensive protocol will detail the workflow for both wet laboratory procedures (sample preparation and imaging) and the proper utilization of our open-sourced computational pipeline (environment setup and processing of the imaging data) to extract and validate ImAge.

Representative images should be well populated to obtain single cells, yet individual nuclei should not be touching each other, since it will degrade the quality of the cell nuclei segmentation. Figure 3 shows the representative imaging to obtain robust computational analysis results.
Results outlined in this methods manuscript are adapted from the ImAge manuscript by Alvarez-Kuglen et al.1. ImAge successfully detected the reversal of aging...

The protocol presents the imaging and computational workflow to extract and validate imaging-based chromatin and epigenetic age (ImAge).

To advance personalized healthcare and assess potential rejuvenation strategies, biomarkers reflecting biological age are crucial. We have introduced ...

We developed imaging-based chromatin and epigenetic age image, a novel technique designed to quantify aging and rejuvenation at single cell resolution. DNA methylation clocks estimate biological age, but depend on linear regression, need large cohorts, and destroy samples, making them costly and unsuitable for large-scale or longitudinal studies. To begin, transfer the frozen organs and tissues to a pre-chilled mortar placed over dry ice.Pour liquid nitrogen over the frozen tissue and grind it thoroughly using a pre-chilled pestle until a uniformly-fine powder is obtained. Using a pre-chilled metal spatula, aliquot the ground tissue into multiple tubes. After extracting the nuclei, mix them with trypan blue in an equal ratio in a separate tube.Transfer 10 microliters of the mixture to a hemocytometer, or load it into an automated cell counter. Enter and verify all metadata, including sample IDs, conditions, and biomarkers in the plate map and export a CSV file. Now, dispense 10, 000 nuclei per well in a 20 to 30 microliter volume into a 384 well plate.Centrifuge the plate at 1000g, with acceleration setting nine and deceleration setting four, for 10 minutes at four degrees Celsius. Next, fix the nuclei by adding an equal volume of 8%paraformaldehyde and PBS, and incubate for 10 minutes at room temperature. After removing the fixative, add 30 microliters of 0.1 molar glycine in PBS and incubate for five minutes at room temperature.Remove the glycine solution and wash the wells twice with 100 microliters of PBS. Remove the PBS from each well and add 30 microliters of blocking solution containing 2%BSA and 0.5%Triton X-100 in PBS. Incubate the plate for one hour at room temperature.After removing the blocking solution, incubate the sample with 25 microliters of primary antibody solution at four degrees Celsius. Next, wash the wells three times with 100 microliters of PBS, each wash lasting three minutes at room temperature. Add 25 microliters of secondary antibody solution and incubate for two hours at four degrees Celsius.Then, wash the wells three times with 100 microliters of PBS, each wash lasting three minutes at room temperature. Stain the nuclei with DAPI or Hoechst by adding 30 microliters of the dye at a concentration of 0.01 milligram per milliliter, and wash the plate once with 100 microliters of PBS to remove excess dye. Download the workflow scripts from the GitHub repository.Once the download is complete, unzip the file to extract its contents. Organize the cloned image repository so that the folder structure matches the format assumed by the workflow for correct script execution. Then, open the Terminal application if operating on Linux or Mac OS, or the WSL application if using Windows.Change the working directory to the cloned repository folder by typing the required command in the terminal and pressing Enter. Create a new computational environment named Image_workflow using Conda with Python version 3.10 and press Enter. Activate the newly-created Conda environment with the command conda activate Image_workflow.Install Poetry, a Python package manager, within the activated environment using the PIP installer. Then, install all prerequisite Python packages required for the workflow using Poetry. Now, configure CUDA within the Conda environment to enable graphics processing unit functionality during the segmentation step.Open the Terminal application and change the folder to the cloned. Activate the Conda environment by typing the command below and pressing Enter. Open the workflow main file in a code or text editor such as VS Code or Notepad to configure the analysis parameters.Set the project name by assigning the variable p to a short file system safe identifier used to organize outputs. Set the list of channels for feature extraction in chs, including the segmentation channel if features from that channel are required. Using ImageIndex and the channel label as the key, map the image channel identifier from file names to the exact case-sensitive plate map column header saved earlier.Then, set data locations by assigning orgDataLoadPath to the dataset route, orgDataSubfolder to the image subfolder if needed, and resultsSavePath to the output route. Confirm the file name extension in imageFileFormat. Then, edit imageFileRegEx to use Python-named capturing groups for the required parts, including row, column, field, Z position, and channel.Now, set the segmentation channel in segmentation_ch to the nuclear channel used for object detection. Decide whether to use illumination correction by setting a illumiCorrection and set to true to compute and apply basic models. Then, set the physical voxel size in micrometers in voxel_dim as ZYX using microscope metadata.Save the file and close the editor after verifying each parameter. Finally, run the workflow script to obtain image readouts and perform validation, keeping the terminal open while the script is running. Visualize results and export raw image readouts for additional analysis in external software.Representative images of induced pluripotent stem cells showed clearly-separated nuclei stained with DAPI, trimethylated histone H3 at lysine 27, and acetylated histone H3 at lysine 27. Image analysis of liver and skeletal muscles revealed a significant difference between young and old samples, with old OSKM mice showing reduced image compared to old, indicating partial reprogramming. Analysis of individual animals indicated that some old OSKM samples displayed significantly-reduced image values compared to old mice.This protocol enables researchers to investigate age-associated changes in chromatin and epigenetic organization at a single cell resolution across different cell types and different tissues. Our imaging-based method is non-destructive, it preserves chromatin structure, and allows for repeated measurements, and is more cost-effective in large-scale or longitudinal studies. Future research will focus on adding more epigenetic markers and studying aging-related, intranuclear structures.

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Research

JoVE Journal

Biology

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Discover imaging-based chromatin and epigenetic age (ImAge) for quantifying aging at single-cell resolution. This non-destructive method enables efficient biological age estimation without sample destruction.

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

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