The primary objective of modern proteomics studies is to analyze and isolate the proteome of individual cells from a pool of similar cells. Existing techniques like RNA sequencing, microarrays, quantitative PCR, and reverse transcription-PCR (qRT-PCR) are designed to analyze RNA in a population of cells in general. These techniques fail to quantify phenotypically different individual cells called “rare cells.” scRNA-seq can analyze RNA expression in a single cell and has been used by pharmaceutical companies in designing new drugs.
Single-cell Analysis and its Application
The existence of phenotypically different, divergent cells in a population of cells is a common occurrence. These variant cell types are termed rare cell types. Next-generation sequencing (NGS) has helped identify and analyze these individual cells. Existing genomic analysis techniques assume tissue to be homogeneous in nature. NGS-based studies proved the heterogenic nature of the cellular activity for similar cell types due to the presence of rare cells. The single-cell RNA (scRNA-seq) method was devised to analyze variant cell types in a given population.
scRNA-seq is presently being researched for its ability to recognize cellular and molecular targets used for drug development. It has been used to identify suitable preclinical models for a particular disease. It can aid in the drug development process, like target qualification and authentication.
scRNA-seq Procedure and its Limitations
The following steps are performed during scRNA-seq analysis:
1) Isolation of single cells and their RNA
2) Performing reverse transcription on the RNA template isolated to generate complementary DNA (cDNA)
3) Performing a polymerase chain reaction (PCR) to amplify cDNA fragments.
4) Generating cDNA libraries, followed by sequencing
Though scRNA-seq is a reliable method of sequencing RNA, at times, identification of individual cells present in minute quantities can be a problem. The sample available may not be enough to generate a cDNA library. More efficient methods of analyzing variant cells in small proportions are needed.
Single-cell (SC) analysis has been efficiently used to understand disease mechanisms at the molecular level. SC sequencing has been used to identify biomarkers. Biomarkers are helpful in various ways and can be used to study disease progression. It can be beneficial for understanding the mechanisms of action of a particular drug and monitoring drug responses. Diseases involve various cell types, like normal and variant or mutated cells. SC analysis refers to the study of an individual cell at a molecular level. SC techniques such as scRNA-seq can identify normal and variant cell types, as well as their phenotypes.
Single-cell Analysis for Diseases
Cancer
SC molecular analysis has been used to study diseases like cancer. Performing SC analysis on cancer cells helped the scientists identify the cell of origin and has been used for studying cancers like renal cell carcinoma, oesophageal adenocarcinoma, prostate cancer, etc. scRNA-seq has identified transcriptional as well as cellular level variation in cells with cancer. It helped track mutations in cancer cells. Multi-omics single-cell analysis of cancer cells has provided extensive information on cellular diversity at transcriptional levels. scRNA-seq has improved our understanding of the metastatic stage of cancer. Using SC analysis on circulating tumor cells, new insights regarding the immune evasion mechanism of circulating tumor cells have been revealed. Similarly, SC mapping of melanoma sentinel lymph nodes identified the immunological factor responsible for relapse in skin cancer.
Parkinson Disease
SC-based analysis was performed on the dopamine-producing neurons present in the substantia nigra portion of the midbrain. The analysis revealed that out of ten identified dopamine-producing subpopulations of cells, only one disintegrates in a Parkinson’s disease setting. This identifying feature may be used to design drugs related to Parkinson’s disease.
Alzheimer Disease
A genome-wide SC-based analysis was performed on 300 independent neurons obtained from the hippocampus and the prefrontal cortex regions of the brain of individuals with known Alzheimer’s disease. The results of the sequencing identified the genomic variation that causes nucleotide oxidation, ultimately leading to the loss of neural functions. Single-cell technology like Patch-seq helps identify subpopulations of cells in the neocortex region of the brain that are destroyed in Alzheimer’s disease. Patch-seq pairs scRNA-seq with VINE-seq (vessel isolation and nuclei extraction for sequencing), along with the additional feature of patch clamp recording. VINE-seq is based on the single-nucleus RNA sequencing (snRNA-seq) method. Another comparative scRNA-seq-based study was performed on brain tissues collected from healthy and Alzheimer’s disease mice. The study proved the involvement of microglia in Alzheimer’s disease progression.
Inflammatory and Autoimmune Diseases
scRNA-seq in studying inflammatory diseases revealed the involvement of regulatory T cells in causing spondyloarthritis, which causes inflammation in the spine and joints. scRNA-seq also helped in detecting variant cytotoxic T cells that caused psoriatic arthritis in the synovial tissues. SC mapping of blood samples taken from patients with rheumatoid arthritis identified two different arthritis subtypes.
Single-cell Analysis-based Drug Target Discovery
The general method for drug target discovery can be summarized as follows:
- The primary step is the identification of the target.
- Followed by target verification and validation.
- Preclinical studies on selected targets.
- Drug screening.
- Biomarker discovery.
SC analysis-based identification of targets related to cancer has made significant advances. SC technology-based analysis of glioblastoma helped identify a new immunotherapy target, S100A4, that is cell-type-specific. Scientists performed an experiment where they deleted the target S100A4 gene from non-cancer healthy cells. This reprogrammed the immune system, resulting in enhanced survival. The use of single-cell data along with computational methods has led to the efficient prediction of cell-specific targets for arthritis.
scRNA-seq data can be used to bridge the gap between potential models and knowledge regarding disease biology. In association with CRISPR technology, single-cell sequencing can improve target validation and refinement. SC sequencing represents a new method of evaluating models or patients by analyzing rare cell phenotypes as opposed to existing protocols performed in a larger setting. SC-based cell expression studies can interpret the molecular mechanism of drug resistance at the level of individual cells. Single-cell RNA sequencing has helped identify predictive biomarkers that can help identify patients who benefit from clinical trials.
Conclusions and Future Implications
scRNA-seq, can help us gain information regarding tumor heterogeneity and understand phenotypes associated with complex diseases. A recent article published in the journal Nature suggests the use of scRNA-seq analysis along with computational tools to identify potential drug targets.
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Sipra Das is a consulting scientific content writing intern at CBIRT who specializes in the field of Proteomics-related content writing. With a passion for scientific writing, she has accumulated 8 years of experience in this domain. She holds a Master's degree in Bioinformatics and has completed an internship at the esteemed NIMHANS in Bangalore. She brings a unique combination of scientific expertise and writing prowess to her work, delivering high-quality content that is both informative and engaging.
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