Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells

John D. Blair, Austin Hartman, Fides Zenk, Carol Dalgarno, Barbara Treutlein, Rahul Satija

Preprint posted on 28 March 2023

Blair and colleagues present a novel scalable sequencing-based multi-omics technique callled Phospho-Seq that involves simultaneous measurement of chromatin accessibility and proteomics, followed by a computational integration of expression data.

Selected by Benjamin Dominik Maier


Profiling of proteins using “multiomic” technology

Multimodal omics techniques involve the simultaneous readout of multiple types of omics data (such as transcriptomics and proteomics) to generate a more comprehensive understanding of biological systems. The first generation of methods to quantify proteins alongside other omics modalities [see CITE-seq (Stoeckius et al., 2017), REAP-seq (Wong et al., 2022), DOGMA-seq (Mimitou et al., 2021) and TEA-seq (Swanson et al., 2021)] were limited to the detection of surface proteins. Although hematopoietic samples can be characterised very effectively with these methods, there is a need for experimental protocols to measure intracellular and intranuclear protein levels in order to inter alia identify signalling cascades and characterise disease states. Some pioneering approaches [see ASAP-seq (Mimitou et al., 2021), inCITE-seq (Chung et al., 2021), NEAT-seq (Chen et al., 2022) and QuRIE-seq (Rivello et al., 2021)] to measure proteins inside cells have been published recently.  However, they are limited in scalability due to reliance on commercially available conjugated antibodies and do not allow for trimodal readout in the same biological system.

Bridge Integration

The Satija lab recently published a preprint (Hao et al., 2022) to  computationally harmonise unimodal single-cell measurements from two omics modalities (RNA and protein, chromatin, histone, or methylation) using separate multi-omics datasets as molecular bridges. The bridge dataset measures both modalities simultaneously, enabling the mapping of single-omics datasets onto each other. This enables the study of relationships between multiple omics readouts and facilitates experimental designs, as multi-omic technologies may only be applied to a subset of the experimental samples while cheaper/high-quality single-omics can be performed for all samples. In this preprint, Blair and colleagues use bridge integration to integrate single cell transcriptomics readouts into Phospho-seq.

Fig. 1 Bridge Integration. Figure taken from Blair et al. (2023), BioRxiv published under the CC-BY-NC-ND 4.0 International license.

Key Findings

Benchtop Preparation of Barcoded Antibodies for Protein Staining

Phospho-seq adapts a benchtop conjugation strategy described in van Buggenum et al. (2016) to generate large custom uniquely-indexed DNA-bound antibody panels for protein staining. As this method is easy to use, cost-efficient and compatible with commercially available antibodies (unconjugated and conjugated), the approach is scalable and suitable for many experimental settings.

Phospho-Seq Workflow

Following dissociation, single cells are subjected to fixation to maintain structural integrity and detergent-based permeabilization enabling oligonucleotide-tagged (barcoded) antibodies to enter the nucleus and the cytoplasm to bind their target proteins. Single-stranded DNA binding protein is added to the antibody pool to prevent nonspecific binding of cellular components to the barcoded antibodies thereby reducing background noise.

Finally, scATAC seq is performed using the Tn5 transposase enzyme and the 10x scATAC-seq kit to identify regions of chromatin that are accessible to transcription factors and other regulatory proteins. The method involves the use of a transposase enzyme that can insert sequencing adapters into open chromatin regions, followed by sequencing to identify the location and accessibility of these regions across the genome.

Fig. 2 Phospho-Seq Workflow. Figure taken and from Blair et al. (2023), BioRxiv published under the CC-BY-NC-ND 4.0 International license and converted into landscape orientation.


In order to improve cell annotation and construct gene regulatory networks, single cell transcriptomics data is required alongside chromatin accessibility and proteomics readouts. Initially, the authors tried to directly generate trimodal readout in Phospho-seq, however they observed a considerable decrease in data quality due to incompatible fixation and permeabilization conditions for RNA and ATAC readout. Hence, they used a recently developed computational approach, bridge integration, to map single cell transcriptomics data to the Phospho-seq data yielding high quality trimodal data.

Phospho-seq Measurements Improve Biological Understanding

The authors demonstrated that the proteomics readout alongside a chromatin accessibility measurement is suitable to distinguish cell types. For instance, they found that the segregation and differentiation capacity of different induced pluripotent stem cell (iPSC) lines can be better explained by their readout instead of epigenetic differences.

Moreover, different distributions of unphosphorylated and phosphorylated proteins were measured upon activation of a signalling cascade indicating that the readout can be used as a proxy for stimulation even when the total abundance of the proteins remained the same.

This is highly relevant as it was also shown in the preprint that for some transcription factors (TFs), phosphorylation levels of nuclear TFs correlated with its activity while no correlation was found between the total abundance and the activity. The authors therefore suggested that phosphorylated TF levels may be more informative with regards to TF activity and reflect cellular states. Finally, the authors demonstrated that Phospho-seq is applicable to a broad range of samples as could be seen by its suitability to cell cultures, iPSCs and organoids.

Conclusion and Perspective

This preprint builds upon recently published multiomics approaches, allowing for the simultaneous characterization of intranuclear, intracellular, and cell surface proteins alongside RNA levels and chromatin accessibility. This method can improve our understanding of intracellular protein dynamics and posttranslational modifications, which cannot be detected by transcriptomics approaches. Furthermore, the method can potentially be combined with other state-of-the-art sequence-based omics approaches to measure more modalities simultaneously. A fixation and permeabilization routine compatible with scATAC and scRNA sequencing techniques could enable direct simultaneous measurement replacing the bridge integration.

Recent advances in sequence-based single-cell transcriptomics have led to the emergence of numerous single-molecule protein sequencing and fingerprinting technologies. This might create the opportunity to study the diversity of proteoforms and to distinguish between different posttranslational modifications, alternative splicing and germline variants in the future.

What I liked about this preprint

It is inspiring to read a preprint in a pioneering field and to see how novel methods build and expand upon previous ones. I found it particularly interesting how the authors integrated their recently published bridge integration into Phospho-seq and how it can help to obtain multimodal data. Additionally, it is beneficial if a method follows the FAIR guidelines, making all machines, reagents, and datasets publicly available and accessible to the community.


Chen, A. F., Parks, B., Kathiria, A. S., Ober-Reynolds, B., Goronzy, J. J., & Greenleaf, W. J. (2022). NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nature Methods, 19(5), 547–553.

Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., … Regev, A. (2021). Joint single-cell measurements of nuclear proteins and RNA in vivo. Nature Methods, 18(10), 1204–1212.

Hao, Y., Stuart, T., Kowalski, M., Choudhary, S., Hoffman, P., Hartman, A., … Satija, R. (2022). Dictionary learning for integrative, multimodal, and scalable single-cell analysis. BioRxiv.

Kelly, R. T. (2020). Single-cell proteomics: Progress and prospects. Molecular & Cellular Proteomics: MCP, 19(11), 1739–1748.

Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., … Smibert, P. (2021). Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nature Biotechnology, 39(10), 1246–1258.

Rivello, F., van Buijtenen, E., Matuła, K., van Buggenum, J. A. G. L., Vink, P., van Eenennaam, H., … Huck, W. T. S. (2021). Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics. Cell Reports Methods, 1(5), 100070.

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., … Smibert, P. (2017). Simultaneous epitope and transcriptome measurement in single cells. Nature Methods, 14(9), 865–868.

Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., … Skene, P. J. (2021). Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. ELife, 10.

van Buggenum, J. A. G. L., Gerlach, J. P., Eising, S., Schoonen, L., van Eijl, R. A. P. M., Tanis, S. E. J., … Mulder, K. W. (2016). A covalent and cleavable antibody-DNA conjugation strategy for sensitive protein detection via immuno-PCR. Scientific Reports, 6(1), 22675.

Wong, M., Kosman, C., Takahashi, L., & Ramalingam, N. (2022). Simultaneous quantification of single-cell proteomes and transcriptomes in integrated fluidic circuits. In Methods in Molecular Biology (Clifton, N.J.). Methods in Molecular Biology (pp. 219–261).

Tags: omics, proteomics, sequencing, single cell

Posted on: 19 April 2023


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Q1: When reading your manuscript, I was wondering if it would be possible to adapt your protocol to study other posttranslational modifications such as glycosylations and which obstacles one would need to overcome before implementation? Additionally, I was thinking whether and how it would be possible to measure specific phosphosites and combinations of those?

As Phospho-seq is an antibody based assay, it should work with any post-translational modification that can be targeted with an antibody, whether it is glycosylated proteins, ubiquitinated proteins or multiple phospho-sites on the same protein. The adaptation would be simple – just conjugate your antibody with a known ssDNA index and include it in your antibody panel. The only obstacle for this, as well as any other protein one wishes to target, is finding an antibody with sufficient enough specificity. Specificity of antibodies in Phospho-seq is similar to other assays such as intracellular flow-cytometry (ICFC) or immunocytochemistry (ICC), so one can rely on the manufacturers data and/or verify specificity with one’s own controlled ICFC and ICC expierments.

Q2: Is it possible to accurately quantify lowly abundant species using Phospho-seq and if so, what is the detection limit?

As with ICFC and ICC, the lower detectable limits of Phospho-seq depends on antibody sensitivity and specificity. There is no consistent lower detection limit between proteins, so for some proteins, it is possible to accurately quantify lowly abundant species, however, for others it is not.

Q3: On page 8, you wrote: “Interestingly, we observed numerous cases where individual genes were regulated by multiple CREs, which were associated with different TFs. This included cases where putatively activating and repressive peaks were adjacently located within the same locus.” – Do you have any biological explanations for this observation?

Our results are compatible with numerous models of mammalian gene regulation that suggest that regulation is complex, context-specific, and dependent on multiple transcription factors (including both activators and repressors). While we did perform mechanistic hypothesis testing as part of this manuscript, we are excited to further explore this result – and in particular to test if there are specific interactions between TFs that may regulate adjacent CRE.

Q4: Phospho-seq is inspired by several previously published techniques, for instance by incorporating the fixation and permeabilization method from ASAP-Seq and the  single-stranded DNA binding protein blocking used in NEAT-seq. How can the insights from the development of Phospho-seq (incl.  bridge integration) help and inspire the development of new and the optimization of existing methods?

We believe that Phospho-seq is an adaptable technology and that the techniques within can be portioned and combined with other technologies for future discovery. For instance – while we use the 10X genomics platform for developing our libraries, all the steps up to loading the cells into a 10X chip should be compatible with many other single-cell technologies including Split-seq or sci-RNA. Additionally, spatial methods, like spatial-ATAC-seq should also be compatible with the fixation and staining steps of Phospho-seq. For optimizing existing methods, we believe the demonstration of cheap and easy antibody conjugation will allow users to expand their antibody panels and obtain more protein data from single-cell protein profiling technologies like inCITE-seq and NEAT-seq.

Rosenberg AB, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018 Apr 13;360(6385):176-182.
Cao J, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017 Aug 18;357(6352):661-667.
Deng, Y., Bartosovic, M., Ma, S. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).
Chung, H., Parkhurst, C.N., Magee, E.M. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat Methods 18, 1204–1212 (2021).
Chen, A.F., Parks, B., Kathiria, A.S. et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat Methods 19, 547–553 (2022).

Q5: With nanoPOTS and the tandem mass tag workflow SCoPE-MS, two mass spectroscopy-based techniques for single-cell proteomics have emerged recently. At the same time, we are observing rapid advances in miniaturised liquid chromatography (HPLC) and gas-phase separation (see Kelly, 2020). What do you think will be the role of sequence-based and mass spectroscopy-based methods in the future? How do they complement each other?

Recent advances in single-cell proteomics are very exciting and as the technology improves and becomes more accessible, they will be integral in unbiasedly surveying the proteome. In contrast to Phospho-seq, the single-cell proteomics methods are unimodal, providing no information on genomic regulation or related processes, despite profiling more overall proteins. However,  we can use bridge integration to create more informative multimodal datasets as demonstrated in Hao, et al. (2023) by using a CITE-seq bridge to integrate unimodal CyTOF protein data with gene expression data. We believe that we can use Phospho-seq as a bridge between single-cell proteomics and genomic modalities.

Hao, Y. et al. Dictionary learning for integrative, multimodal, and scalable single-cell analysis. Preprint at

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