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Updated: Oct 3, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
Optimising protein detection with fixable custom oligo-labelled antibodies for single-cell multi-omics approaches
Iivari Kleino1, Kirsten Nowlan2, Juha Kotimaa3
1Translational Immunology Research Program, University of Helsinki, Helsinki, Finland.
Background And Aim:
Single-cell RNA sequencing (scRNA-seq) is a powerful method utilising transcriptomic data for detailed characterisation of heterogeneous cell populations. The use of oligonucleotide-labelled antibodies for targeted proteomics addresses the shortcomings of the scRNA-seq-only based approach by improving detection of low expressing targets. However, optimisation of large antibody panels is challenging and depends on the availability of co-functioning oligonucleotide-labelled antibodies.
Main Methods And Results:
We present here a simple adjustable oligonucleotide-antibody conjugation method which enables a desired level of oligo-conjugation per antibody. The mean labelling in the produced antibody batches varied from 1 to 6 oligos per antibody. In the scRNA-seq multimodal experiment, the highest sensitivity was seen with moderate antibody labelling as the high activation and/or labelling was detrimental to antibody performance. The conjugates were also tested for compatibility with the fixation and freeze storage protocols. The oligo-antibody signal was stable in fixed cells indicating the feasibility of a stain, fix, store, and analyse later type of workflow for multimodal scRNA-seq.
Conclusions And Implications:
Optimised oligo-labelling will improve detection of weak protein targets in scRNA-seq multimodal experiments and reduce sequencing costs due to a more balanced amplification of different antibody signals in CITE-seq libraries. Furthermore, the use of a pre-stain, fix, run later protocol will allow for flexibility, facilitate sample pooling, and ease logistics in scRNA-seq multimodal experiments.
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