HiExM Enables Scalable Mapping of Organelle Morphology and Spatial Heterogeneity
Posted on: 19 October 2026
Preprint posted on 14 July 2026
HiExM meets machine learning, revealing cellular patterns beyond the average.
Selected by Dina Kabbara
Categories: bioinformatics, cell biology
Background
Studying individual organelles at scale is hard. Many sit near the diffraction limit of light (~200 nm), so neighboring organelles often blur together and are difficult to segment (Sahl et al., 2017). Electron microscopy resolves ultrastructure but does not scale easily or allow protein-specific, multiplexed measurements (Klumperman et al., 2014). Super-resolution methods are often too slow or have too small a field of view (Sahl et al., 2017). Most studies therefore fall back on bulk measurements, such as overall intensity or colocalization, which hide organelle-to-organelle variability.
Late endosomes and lysosomes (LELs) illustrate the problem. They range from ~100 nm to over 1 µm and are densely packed (Klumperman et al., 2014). Their function varies with size, position, and clustering (Bussi et al., 2024). LAMP1, a protein on their membrane used as a fluorescent tag, marks the whole compartment but cannot separate one organelle from the next at conventional resolution (Bond et al., 2025).
Expansion microscopy (ExM) physically enlarges the sample, giving ~4-fold higher effective resolution while preserving labeling (Chen et al., 2015). High-throughput ExM (HiExM), developed previously by the same group as the preprint highlighted here, adapts this to multi-well plates for automated imaging (Day et al., 2024). Now, in their latest preprint, here, the authors combined HiExM with single-organelle segmentation and machine learning, using LELs as a benchmark. They asked whether HiExM reveals antibody-trafficking differences that conventional colocalization misses and whether organelle shape and cellular position are linked.
Key Findings
HiExM maps individual organelles.
The authors used HiExM and obtained a ~4-fold expansion of their samples and clearly improved resolution of LAMP1-positive organelles (preprint Figure 1A-C). Individual LELs could be distinguished in crowded regions, and features such as the lumen became visible (preprint Figure 1E-F). The authors manually generated ground-truth segmentations and trained a StarDist model to identify single LELs (preprint Figure 1D). The resolution also showed LAMP3 concentrated in the lumen, while LAMP1 peaked at the membrane (preprint Figure 1G-H).
Figure 1D. Application of the StarDist segmentation model to the same optical section following expansion, enabling the identification of individual organelles for subsequent morphological analysis. Made available under a CC-BY 4.0 International license.
Single-organelle analysis reveals antibody trafficking differences
The authors tracked three anti-transferrin receptor (TfR) antibodies: bivalent (AbB), monovalent (AbM), and an AbM-GAA, which is basically an AbM linked to the enzyme alglucosidase alfa. Confocal imaging could not distinguish AbB from AbM in LAMP1 colocalization (preprint Figure 2A-B), but HiExM showed greater colocalization for AbB as early as 90 minutes (preprint Figure 2C-D). AbM-GAA showed the greatest volumetric overlap with LAMP1 segmentations (preprint Figure 2E-F). Per-organelle analysis revealed what bulk colocalization missed: AbB shifted the intensity distribution across all LELs, whereas AbM-GAA produced a lengthened tail, indicating accumulation in a smaller subset of LELs (preprint Figure 2G-H).
Figure 2C–D. HiExM-based analysis of antibody localization within late endosomes and lysosomes reveals differences in intracellular trafficking that are difficult to distinguish using conventional confocal microscopy. Made available under a CC-BY 4.0 International license.
Morphology and spatial organization are linked.
The authors measured five features per LEL: volume, distance from the nucleus, distance from the cell membrane, number of neighboring LELs, and connectivity. Across 89 cells, these sorted LELs into 14 clusters, and every cell contained most of them. This means that diversity exists within cells, not just between them (preprint Supplementary Figures 7-8). Larger LELs tended to be more connected, while peripheral ones were smaller and less connected (preprint Figure 3F).
Because these features were chosen in advance, the authors also trained a 3D variational autoencoder (VAE) on 100,000 LEL images, turning each into 256 numbers (preprint Figure 3C). This gave 16 morphologically distinct clusters (preprint Figure 3D). Several measured-learned pairings occurred more than two-fold above or below chance (preprint Figure 3E-F). For example, large LELs (measured cluster 7) were enriched for learned cluster J, which also corresponds to large organelles, suggesting that an LEL’s position and appearance are linked.
Figure 3F. Representative pairings of measured-feature clusters and learned-feature clusters reveal links between LEL spatial organization and morphology. Measured cluster 7, characterized by large organelles, is enriched for learned cluster J, which also represents large LELs. Each pairing combines a five-feature z-score profile with the decoded mean morphology and a representative organelle (scale bar: 1 µm). Made available under a CC-BY 4.0 International license.
Machine learning distinguishes lysosomal perturbations.
Cells were treated with apilimod, bafilomycin, or chloroquine, which disrupt lysosomes by different mechanisms, and compared with DMSO controls (preprint Figure 4A-C). With the five measured features, no cluster differed by more than two-fold between compounds (preprint Figure 4D). With learned features, several clusters were more than two-fold enriched or depleted (preprint Figure 4E). In short, the drugs rearrange LELs similarly in size and position but change their appearance differently, and only the learned features detected this.
One measured-learned pairing was depleted with chloroquine but enriched with bafilomycin or apilimod, so it could help tell the drugs apart (preprint Figure 4F-G). The authors also described each cell by the share of its LELs in each cluster, a compositional fingerprint of organelle state. Fingerprints from learned clusters separated treatments better than those from measured features (preprint Figure 4H-I). Organelle-level imaging could therefore give a richer readout of drug effects than narrow measures such as viability.
Figure 4D–E. Comparison of treatment-induced changes in measured-feature clusters (D) and learned-feature clusters (E) following apilimod, bafilomycin, or chloroquine treatment. Made available under a CC-BY 4.0 International license.
What I like about this preprint
The way this study approaches a blurry-image problem as a critical thinking exercise is what I find most appealing. The autoencoder learns what the measurements miss, segmentation makes each lysosome quantifiable, and HiExM allows the authors to visualize individual lysosomes. Learning, measuring, and seeing come together as one cohesive argument rather than three separate techniques.
What stands out to me most is how often a bulk average concealed meaningful differences. Standard confocal microscopy could not distinguish between the bivalent and monovalent anti-TfR antibodies, and even after enlargement, volumetric overlap suggested only that AbM-GAA was “more colocalized.” However, the per-organelle histograms revealed that this pattern was concentrated in a small subset of LELs. A single colocalization value averages away this variation. The drug experiments make a similar point: apilimod, bafilomycin, and chloroquine appeared almost identical in size and position, yet the learned features revealed distinct effects on lysosomes. In other words, two treatments can appear similar when measured in one way but differ when examined in greater detail.
My main hesitation is that the biological meaning of the learned clusters remains unclear. The study shows that the drugs affect lysosomes differently, but it is less clear what those differences represent biologically. I would be particularly interested to see whether the learned clusters correspond to LEL subpopulations with known molecular characteristics, as this could help explain what the model is actually detecting.
Future directions
The most obvious next step is linking the learned morphological states to function. LAMP1 is a broad marker, so the clusters show how LELs look rather than what they do. Mapping molecularly defined subpopulations onto the learned feature space would close that gap, with better universal stains and expansion-optimized antibodies to make it possible.
Future studies could also test whether the same morphology-position relationships hold in other cell types and extend the pipeline to mitochondria and nuclei, ideally imaging several organelle classes in the same cell. Finally, the authors’ own data suggest testing whether learned organelle features can predict outcomes in drug and antibody screening, not just describe them.
Questions for the authors
Q1: The machine-learning model found that the drugs change organelles in different ways, but we don’t know what those changes mean. How do you plan to figure out what each group of organelles actually does, for example, by adding stains for specific proteins?
Q2: You studied one type of cell. Do you think organelles look and sit the same way in other cell types, or might each cell type arrange its organelles differently for its own job?
Q3: Last but not least, could organelle-level learned representations serve as predictive phenotypic readouts for therapeutic screening, rather than only descriptive measurements?
References
1- Sahl, S. J., Hell, S. W., & Jakobs, S. (2017a, September 6). Fluorescence nanoscopy in cell biology. Nature News. https://www.nature.com/articles/nrm.2017.71
2- Klumperman, J., & Raposo, G. (2014). The complex ultrastructure of the endolysosomal system. Cold Spring Harbor Perspectives in Biology, 6(10). https://doi.org/10.1101/cshperspect.a016857
3- Bussi, C., & Gutierrez, M. G. (2024a). One size does not fit all: Lysosomes exist in biochemically and functionally distinct states. PLOS Biology, 22(3). https://doi.org/10.1371/journal.pbio.3002576
4- Bond, C., Hugelier, S., Xing, J., Sorokina, E. M., & Lakadamyali, M. (2025, January 6). Heterogeneity of late endosome/lysosomes shown by multiplexed DNA-paint imaging. The Journal of Cell Biology. https://pmc.ncbi.nlm.nih.gov/articles/PMC11533445
5- Chen, F., Tillberg, P. W., & Boyden, E. S. (2015). Expansion microscopy. Science, 347(6221), 543–548. https://doi.org/10.1126/science.1260088
6- Day, J. H., Della Santina, C. M., Maretich, P., Auld, A. L., Schnieder, K. K., Shin, T., Boyden, E. S., & Boyer, L. A. (2024). HiExM: High-Throughput Expansion Microscopy Enables Scalable Super-Resolution Imaging. https://doi.org/10.7554/elife.96025.3
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