Expanding Protein Marker Detection with Single-Cell Spatial MIST

Simon Pavlov^*
*Correspondence: Simon Pavlov, Department of Biomedical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece, Email:

Introduction

In the realm of cellular analysis and molecular biology, the identification and quantification of protein markers play a pivotal role in understanding cellular behavior, disease progression and therapeutic responses. Recent advances in single-cell analysis have revolutionized our ability to delve into the intricate heterogeneity of cell populations. One innovative technology that has garnered attention is Single-Cell Spatial MIST (Marker Identification through Spatial Transcriptomics), a cutting-edge approach designed to enable versatile and scalable detection of protein markers within individual cells [1]. Traditional protein marker detection techniques often involve the use of antibodies and staining protocols, which can be limited by their specificity, sensitivity and potential alteration of cellular integrity. Single-Cell Spatial MIST overcomes these limitations by harnessing the power of spatial transcriptomics, where the spatial arrangement of cellular RNA is used as a proxy for protein marker presence. This novel approach has the potential to revolutionize our understanding of cellular interactions and tissue organization, opening new doors for disease research, drug development and personalized medicine [2,3].

Description

Single-Cell Spatial MIST represents a novel and ingenious fusion of Single- Cell RNA Sequencing (scRNA-seq) and spatial transcriptomics technologies. This approach capitalizes on the inherent relationship between mRNA and protein expression levels, leveraging the well-established link between cellular RNA and the proteins they encode. By analyzing the spatial distribution of mRNA molecules within the context of individual cells and tissues, Single-Cell Spatial MIST offers a high-resolution view of the protein markers' presence across diverse cell types and microenvironments [4]. This technique begins with the acquisition of high-quality spatial transcriptomics data, capturing the RNA landscape of tissue sections or cell cultures. Subsequently, advanced computational algorithms are employed to infer the probable presence of specific protein markers based on the co-located mRNA transcripts. The resulting spatial protein marker maps provide an unprecedented glimpse into the heterogeneity and localization of these markers within complex biological systems. Importantly, Single-Cell Spatial MIST is scalable, allowing for its application across a wide range of sample sizes and experimental setups [5].

Conclusion

The emergence of Single-Cell Spatial MIST marks a significant leap forward in our ability to comprehend the intricate interplay of protein markers within the complex tapestry of cellular biology. By circumventing the limitations of traditional protein marker detection methods, this innovative approach brings spatial transcriptomics to the forefront, enabling the versatile and scalable identification of protein markers at the single-cell level. The resulting insights into cellular heterogeneity, microenvironmental influences and disease-associated changes hold immense promise for advancing our understanding of various physiological and pathological processes. As Single-Cell Spatial MIST continues to evolve and integrate with other omics technologies, it is poised to reshape the landscape of molecular research, ultimately paving the way for more precise diagnostics, targeted therapeutics and a deeper comprehension of the fundamental principles governing life.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

References

1. George, Justin and Jun Wang. "Assay of genome-wide transcriptome and secreted proteins on the same single immune cells by microfluidics and RNA sequencing." Anal Chem 88 (2016): 10309-10315.

Google Scholar, Crossref, Indexed at

2. Gry, Marcus, Rebecca Rimini, Sara Strömberg and Anna Asplund, et al. "Correlations between RNA and protein expression profiles in 23 human cell lines." BMC Genom 10 (2009): 1-14.

Google Scholar, Crossref, Indexed at

3. Vogel, Christine and Edward M. Marcotte. "Insights into the regulation of protein abundance from proteomic and transcriptomic analyses." Nat Rev Genet 13 (2012): 227-232.

Google Scholar, Crossref, Indexed at

4. Schoof, Erwin M., Benjamin Furtwängler, Nil Üresin and Nicolas Rapin, et al. "Quantitative single-cell proteomics as a tool to characterize cellular hierarchies." Nat Commun 12 (2021): 3341.

Google Scholar, Crossref, Indexed at

5. Lin, Jia-Ren, Benjamin Izar, Shu Wang and Clarence Yapp, et al. "Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes." Elife 7 (2018).

Google Scholar, Crossref, Indexed at