New STAMP technology bridges single-cell genomics and cell morphology to democratize spatial biology analysis
Study presents analytical workflow linking gene expression with measurable cell shape and size, enabling validation of transcriptional states against imaging data
image: Figure 1. STAMP workflow and downstream analysis. Dissociated cells or nuclei are immobilized on a slide, imaged (RNA +/- protein), and exported as an analysis-ready cell × feature matrix linked to per-cell morphology and coordinates. Downstream Python analysis applies standard single-cell steps (QC, normalization, embedding/latent modeling, clustering/label transfer, morpho-transcriptomic analysis, and interpretation), with results traceable back to the images. STAMP: single-cell transcriptomics analysis and multimodal profiling; QC: quality control; PBMC: peripheral blood mononuclear cell; PCA: principal component analysis; UMAP: Uniform Manifold Approximation and Projection; DE: differential expression; scVI: single-cell variational inference; scANVI: single-cell ANnotation using variational inference.
Credit: © Suresh Poudel*,Felipe Segato Dezem,Luciano G. Martelotto ,Jasmine T. Plummer,Douglas R. Green* 2026. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
A research team at St. Jude Children's Research Hospital, has introduced a downstream analytical framework for STAMP (Single-cell Transcriptomics Analysis and Multimodal Profiling), an emerging technology that preserves cell morphology alongside gene and protein expression. In a mini review published in EXO – Beyond the Cell, the team demonstrates how STAMP data can be analyzed using standard Python-based single-cell tools to link molecular profiles directly with image-derived phenotypes—such as cell area, shape, and marker localization.
Unlike conventional single-cell RNA sequencing, which requires tissue dissociation and loses spatial context, STAMP immobilizes dissociated cells or nuclei on a slide, enabling high-content imaging before molecular readout. This preserves per-cell visual characteristics while allowing high-plex transcript and protein measurement. The result is a unified dataset where each cell is represented by both its gene expression vector and its morphological features.
The team validated a complete computational pipeline using a publicly available pooled mixture of three cancer cell lines (LNCaP, MCF7, and SKBR3) as a test case. They applied quality control informed by both molecular and imaging data, normalized expression, performed dimensionality reduction, and used semi-supervised label transfer (scVI and scANVI) to project unlabeled mixture cells onto pure reference cell line embeddings. The method recovered expected cell-type identities with high confidence and revealed that ambiguous or low-confidence assignments were extremely rare (0.095% of cells had posterior probability <0.8).
A key innovation highlighted in the work is morpho-transcriptomic coupling—explicitly testing whether transcriptionally inferred states correlate with co-measured morphology. For example, the team showed that a G2/M proliferation module score correlated with cell area in a cell-line–dependent manner, with effect sizes ranging from r = −0.18 to r = 0.19. This approach transforms morphology from a qualitative illustration into a measurable covariate that can confirm or challenge transcript-based conclusions.
The team emphasize that STAMP does not preserve native tissue geography (unlike intact-section spatial transcriptomics), but it offers scalability, experimental flexibility, and the unique ability to ground molecular inferences in directly imaged cellular phenotypes. The workflow is compatible with mainstream Python single-cell ecosystems such as Scanpy, scVI, and scANVI.