Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review):
Summary:
Liao et al. present SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), a method for reconstructing spatial organization from diffusion-defined DNA barcode interactions without the use of optical imaging. In SCOPE, hydrogel beads bearing unique DNA barcodes contain both "sender" and "receiver" oligonucleotides. Upon enzymatic release, sender oligos diffuse locally and hybridize to receiver oligos on neighboring beads, forming chimeric molecules that encode spatial proximity. Sequencing these products yields an interaction matrix, which is then used to reconstruct a spatial coordinate map.
The authors demonstrate reconstruction of synthetic two-dimensional shapes, a large multicolor Snellen eye chart, and the interior surface of three-dimensional molds. The work expands the conceptual and experimental landscape of optics-free spatial sequencing.
Thank you for this accurate summary of the work.
Strengths:
SCOPE employs bidirectional sender and receiver oligonucleotides on every bead, rather than using asymmetric transmitter-receiver architectures found in other diffusion-based methods. The symmetric design may improve detection sensitivity and reconstruction strategies, and represents a meaningful variation on optics-free spatial encoding.
A notable strength of this study is the physical scale achieved. The authors reconstruct a Snellen chart spanning approximately 704 mm² and demonstrate molded 3D structures on the order of 75-100 mm³. Although some larger-scale warping is evident, and is discussed as potentially due to non-uniform diffusion, the relative local positioning across these large areas appears impressively accurate.
The authors extend reconstruction beyond two-dimensional arrays to three-dimensional molded surfaces. This demonstrates that the assay and the computational methods for interpreting proximity graphs can support nonplanar spatial relationships, expanding the scope of optics-free spatial inference.
Thank you for highlighting these strengths of SCOPE.
Weaknesses:
Although the method is discussed in the context of spatial genomics and potential tissue applications, it is currently demonstrated only on engineered two-dimensional bead arrays and three-dimensional shapes fabricated in molds. It remains unclear how SCOPE would perform in heterogeneous biological environments, where diffusion may exhibit additional non-uniformities. A biological proof-of-concept, even limited in scope, would help define the method's strengths and limitations more clearly.
We concur with the reviewer that a biological proof-of-concept is a key next step, and that diffusion will be more heterogeneous in this more complex environment. To this end, we are actively working to further develop SCOPE for use in tissue sections, with the goal of capturing transcriptomes, accessible chromatin, and genomes. As part of this work, we also hope to systematically explore a range of tissue permeabilization and tissue clearing approaches to mitigate the impact of heterogeneity on performance.
The reconstruction of three-dimensional structures lacks strong sampling from volume interiors. This is speculated to be due to several possible factors; however, this limitation constrains the method to reconstruction of volume surfaces rather than comprehensive three-dimensional profiling.
Thank you for highlighting this important limitation. The 3D reconstructions are indeed constrained by undersampling of volume interiors. We anticipate that this might be addressed via relatively minor adjustments to the protocol, e.g. using light- or base-labile linkers to trigger oligo release, with the expectation that this will improve reaction consistency throughout the volume. However, even if we are unable to resolve this issue, we note that surface-resolved reconstructions may be useful for some goals, e.g. embedding a bead-packed gel within a tissue lumen, such as the gut. This could enable surface beads to capture RNA transcripts from adjacent cells, while bead–bead associations serve to define the surface topology.
The reconstruction workflow involves multiple preprocessing steps and embedding choices. While these appear to work well for synthetic shapes with known geometry, it is less clear how parameter choices would be made in contexts where ground truth is unknown. Clarifying how reconstruction robustness is assessed without prior knowledge of spatial structure would help readers understand how the method could be practically deployed, particularly in more heterogeneous tissue contexts.
Thank you for the opportunity to clarify. The computational pipeline used for 2D SCOPE reconstruction is designed to operate on a standardized input format and can be applied to arbitrary datasets without prior knowledge of spatial structure. For example, as shown in Figure 3, both the circle and “swoosh” geometries were reconstructed using the same algorithm and identical initial parameters. While certain hyperparameters are pre-specified (e.g. the number of k-nearest neighbours used to compute the pairwise distance matrix for UMAP), these are fixed across datasets. Other parameters, such as UMAP’s “min_dist,” are selected via an automated heuristic grid search that proceeds without user intervention. The agreement with ground truth in these controlled settings, together with the reproducibility of stochastic reconstructions (see Figure 3E-F), supports the robustness of the approach.
Importantly, there was one exception. Reconstruction of the Snellen eye chart dataset required a manual step, involving an initial 3D UMAP embedding followed by a 2D projection to “flatten” the result. We suspect this reflects radial non-uniformities in sender/receiver oligo diffusion at larger spatial scales. Addressing such confounders algorithmically by explicitly modelling diffusion heterogeneity represents an important area for future work, with the goal of entirely eliminating the need for manual intervention.
Finally, we note that these benchmark shapes represent somewhat contrived examples, and the geometries encountered in practice may often be much less complex. For example, in conventional spatial genomics, the geometry consists of a bead monolayer forming a flat, regular surface on a rectangular slide of known dimensions. Regardless of the tissue architecture overlaid on this surface, the reconstruction problem is defined by the bead monolayer itself, inferred through sender-receiver interactions.
Recommendations for the authors:
Reviewer #1 (Recommendations for the authors):
It would be helpful to further clarify the limitations in interior sampling of three-dimensional structures by providing a more explicit comparison with Qian and Weinstein's volumetric DNA microscopy (UMI-UEI) approach. In particular, do the authors anticipate that the current limitation in SCOPE's interior sampling can be mitigated through experimental optimization, or might this represent an inherent challenge associated with hydrogel-based bead scaffolds relative to substrate-free approaches? A more detailed discussion of this point would help readers understand whether the observed volumetric constraint is technical and potentially solvable, or structural to the platform design.
We thank the reviewer for this suggestion and agree that a more explicit comparison to volumetric DNA microscopy helps clarify the origin of the current limitation in interior sampling. Based on our experiments to date, we view this constraint as primarily technical and, in principle, addressable.
A key distinction between SCOPE and volumetric DNA microscopy(Qian and Weinstein, 2025), using the UMI– UEI framework, lies in the recovery of recorded molecules from the interior of the sample. In volumetric DNA microscopy, the hydrogel-embedded specimen can be fully digested and treated with Proteinase K, enabling efficient liberation and recovery of molecules throughout the volume for downstream sequencing(Qian et al., 2026). In contrast, in the current implementation of SCOPE, we do not dissolve the polyacrylamide hydrogel, and recovery therefore relies largely on diffusion of chimeric molecules out of the scaffold. This likely biases against molecules generated in the interior and leads to reduced sampling of internal regions. This interpretation is supported by a control experiment in which barcoded beads were allowed to settle in solution at the bottom of a tube in the absence of a polymerized hydrogel scaffold. In this setting, 3D UMAP reconstruction yielded a solid, non-hollow structure consistent with the expected conical geometry of the tube bottom, indicating that SCOPE is capable of recovering volumetric structure when recovery is not diffusion-limited. Taken together, these observations suggest that the apparent “hollowing” in current 3D reconstructions reflects a limitation in molecule recovery from hydrogel scaffolds, rather than an inherent constraint of the SCOPE framework itself.
We are currently exploring potential solutions, including the use of reducible crosslinkers to enable hydrogel dissolution and/or mechanical shearing of the gel. If these experiments are successful, we would plan to include the results in revisions to the manuscript, together with an appropriately edited version of the paragraph above. If they are unsuccessful and major experimental effort is going to be required to address this issue, we would likely move forward with textual changes only, incorporating the points made in the paragraph above into the discussion.
References
Qian N, Li J, Yasser R, Yu M, Weinstein JA. 2026. Volumetric DNA microscopy for mapping spatial transcriptomes in three dimensions. Nat Protoc. doi:10.1038/s41596-025-01329-3
Qian N, Weinstein JA. 2025. Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy. Nat Biotechnol 1–11.
