Examples of Disease Understanding Through RNA-seq

Tumors: scRNA-seq has proven to be an invaluable tool in understanding the mechanisms behind tumor development. Its application in tumor analysis has shed light on previously overlooked or underappreciated cell populations. For instance, scRNA-seq studies of pancreatic ductal adenocarcinoma (PDA) highlighted the importance and heterogeneity of cancer-associated fibroblasts (CAFs). It was discovered that chemotherapy specifically activates a certain subtype of CAFs, worsening PDA symptoms. Additionally, the study revealed the involvement of the PD-L1-PIGF/VEGF (Placental growth factor/Vascular endothelial growth factor) axis, leading to the identification of new biomarkers for PDA [17, 18]. scRNA-seq has also significantly contributed to the understanding of patient-derived xenograft (PDX) tumor models. In a scRNA-seq-based study of a PDX model of breast cancer, it was found that the onset of triple-negative breast cancer (TNBC) is particularly associated with P53 mutations and chemotherapy, influencing the criteria for chemotherapy diagnosis in breast cancer patients [19].

In tumors, the harsh conditions of the tumor microenvironment can induce epigenetic changes in various cells. Therefore, combining scRNA-seq with single-cell epigenetic analysis techniques (such as scATAC-seq and scChIP-seq) enables a more comprehensive understanding of tumorigenesis. One notable example involved simultaneous scRNA-seq and scATAC-seq analysis of breast cancer tissue and metastasized lymph nodes, which revealed that the expression of the CXCL14 chemokine protein is linked to breast cancer development and metastasis. Interestingly, CXCL14 expression in breast cancer tissue was found to increase over time, with epigenetic regulatory mechanisms playing a key role in this process. As a result, there are ongoing efforts to develop therapies targeting either CXCL14 itself or the epigenetic regulators involved in its expression [20]. Beyond these examples, the mechanisms of cancer development and metastasis across various tumor types are being explored in detail through single-cell analysis techniques. The data from such studies are compiled in databases like the Tumor Immune Cell Atlas, Human Tumor Atlas Network, and Pan-Cancer Blueprint, which researchers can use to generate new hypotheses.

Autoimmune Diseases: Single-cell analysis has provided valuable insights into understanding autoimmune diseases like arthritis. In a study using scRNA-seq to analyze immune cells from the synovium of patients with psoriatic arthritis, it was found that many T cells shared the same clonotype [27]. Similarly, scRNA-seq revealed changes in T cells in rheumatoid arthritis, suggesting that key immune regulators such as ORMDL3 and CTLA4, expressed in specific T cell subtypes, may play a significant role in the disease [28]. Single-cell analysis has also proven useful in studying autoimmune diseases like multiple sclerosis. scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from multiple sclerosis patients highlighted the importance of the relationship between monocyte subtypes and naive T cells. Notably, the cytokine IL-2 was implicated as a possible mediator in this relationship, further advancing our understanding of multiple sclerosis [29].

Neurodegenerative Diseases: In Parkinson’s disease, the degeneration of dopamine-responsive neurons is well-known. scRNA-seq analysis of these neurons revealed that out of 10 subtypes, surprisingly, only one subtype disappears during Parkinson’s disease progression. This lost subtype was also found to express known Parkinson’s disease-inducing factors at very high levels. Current research focuses on understanding the unique biological properties, origin, and differentiation process of this dopamine-responsive neuron subtype, with the aim of developing targeted therapies [30]. To explore the genetic contributions to Alzheimer’s disease, both scRNA-seq and scDNA-seq were used. This analysis suggested that genetic changes in neurons located in the hippocampus and prefrontal cortex may be key factors in the onset of Alzheimer's. These findings have led to further studies investigating the biological characteristics of these neurons and the potential use of their genetic alterations as predictors of Alzheimer's disease [31]. In addition, scRNA-seq analysis of mouse models of Alzheimer’s has shown that microglia in the brain may be deeply involved in the disease’s progression, spurring the development of cell-based therapies targeting this mechanism [32].

Infectious disease: An analysis revealed that in patients with mild symptoms, platelet aggregation and the presence of T cells known as follicular helper T cells were observed, while in patients with severe symptoms, a significant change in CD8+ T cells was identified [21]. Specifically, it was found that within the CD8+ T cell population, some cells can directly recognize COVID-19-derived antigens, while others cannot directly recognize these antigens but are activated by cytokines such as IL-15. The immune response to COVID-19 is determined by the activity of these T cells [22, 23]. Additionally, it was surprisingly discovered that a protein called C3a increases during COVID-19 infection. C3a has been found to promote the differentiation of T cells expressing CD16, and the presence of CD16-expressing T cells has been linked to the severity of COVID-19 symptoms [24].