(F-234) Single-cell Detection of Proteins and Their Spatial Distribution on Mice Tissue using a Novel Microchip Assay

Introduction:: Proteomics is the study of proteome structure, function and interactions. It involves the identification of potential biomarkers and protein expression patterns that can be used to detect disease processes and aid in diagnosis¹. Studying the protein expression profile of single cells will allow for a detailed analysis of cell-cell interactions as well as the heterogeneity between cells. These understanding can improve research areas focused on disease diagnostics, drug discovery and ultimately patient prognosis². The prevailing immunostaining-based approaches allow for the detection of only a few proteins at a time, and thus most biological information is lost³. While single-cell sequencing technologies have existed, they cannot replace the importance of proteins in the clinical diagnosis and mechanistic studies³. However, highly-multiplexed single cell protein analysis, particularly those at the functional proteome level, has not been available on a large scale. Here we demonstrated a method to not only detect the expression of proteins, but their spatial distribution as well on intact kidney sections. The expression of 20 proteins were co-detected and compared between a diabetic mouse kidney section and an aged matched healthy control.


Materials and Methods:: Recently Dr. Wang’s research lab has developed a microchip assay that allows for a comprehensive evaluation of cellular functions and physiological status. The single-round labeling and multi-cycle decoding technique based on the spatial multiplex in situ tagging (MIST) platform allows for the detection of hundreds of proteins simultaneously. The process involves a cocktail of UV-cleavable DNA barcoded antibodies which stain the tissue³. After each staining, the DNA oligos can be liberated by UV light and be “printed” on the MIST array which comprises of microbeads conjugated with complimentary DNA sequences³. Using computer software, the released DNA is decoded based on a predetermined color code scheme where each protein corresponds to a unique order in which the MIST array beads change color (supplemental figure).


Results, Conclusions, and Discussions:: Results and Discussion:
The location of single cells of intact tissue were identified on the spatial MIST array. By using the location of the single cells, the protein expression on those cells were able to be observed based off the way the beads changed color. The differences in protein expression between healthy kidney section and diseased section were then analyzed (supplemental figure). We examined 20 proteins on two kidney sections, one from a diabetic mouse (db/db) and another from an age-matched wildtype mouse (db/+). It was found that NOL3 has very different expression levels between the wildtype and the diabetic kidneys (Figure 3A). The NOL3 protein, also known as Apoptosis Repressor with CARD Domain (ARC), is in-volved in inhibiting apoptosis, or programmed cell death, and is expressed in a variety of human tissues [30, 31]. The reconstructed images displaying NOL3 expression on MIST arrays reveal distributions and levels of expression that are consistent with those observed in the immunostained tissue images. The cells were clustered and separated by UMAP. Cluster 3 highlighted in Figure 3B exhibited increased NOL3 expression in the diabetic kidney section compared to the wildtype kidney section, as many more pseudocells are observed in this cluster (Figure 3B). This elevation could be attributed to enhanced apoptotic pathway signaling in diabetic kidney disease, which is believed to contribute to the progression of the disease pathology.



Conclusion

We have demonstrated a method for co-detecting protein expression and spatial distribution on mice tissue at a single-cell level. While only 20 proteins have been detected for this project, we believe that the current method can be used to co-detect dozens of proteins in the future which would make High Throughput Screening significantly faster and cheaper.


Acknowledgements (Optional): :

References (Optional): : Citations

1 Kwon, Y. W. et al. Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Front Med (Lausanne) 8, 747333, doi:10.3389/fmed.2021.747333 (2021).

2 Chen, X. & Yang, Z. in Biosensors for Single-Cell Analysis (eds Jian Chen & Yao Lu) 37-70 (Academic Press, 2022).

3 Reddy, R., Yang, L., Liu, J., Liu, Z. & Wang, J. Spatial MIST Technology for Rapid, Highly Multiplexed Detection of Protein Distribution on Brain Tissue. bioRxiv (2022).