Genome-wide association studies (GWAS) have been effective in identifying genetic variants linked to complex diseases. While these genotypes reveal disease susceptibility, pinpointing the specific genes and pathways influenced by these variants is challenging. Integrating RNA sequencing (RNA-Seq) allows researchers to annotate and prioritize GWAS-identified variants for functional analysis, aiding in understanding disease mechanisms. Analyzing gene expression shows whether and when genes of interest are upregulated or downregulated in disease samples. This multiomic approach enhances drug target identification and biomarker discovery in functional genomics.
Authors in this Nature Medicine paper were interested in how the SS18-SSX fusion influences the interaction between cancer cells and the immune system in synovial sarcoma (SyS). They tried to understand the tumor microenvironment's complexity by separately analyzing and integrating single-cell RNA sequencing (scRNA-seq) data with genetic analysis. The scRNA-seq identified and characterized different cell populations within the tumor, including a malignant subpopulation that creates immune-deprived niches. The genetic analysis provided insights into the role of the SS18-SSX fusion in regulating this malignant cell state and its impact on clinical outcomes. This dual approach is crucial for dissecting the heterogeneity within SyS tumors and for understanding the mechanisms of immune evasion driven by oncogenic processes.
In the context of solid organ transplant, the longevity and impact of donor-derived immune cells on rejection are uncertain. Traditional methods for differentiating between donor and recipient immune cells were restricted to transplants with gender mismatches, utilizing Y chromosome identification. The whole-exome sequencing (WES) was used to detect genetic differences between donor and recipient, coupled with sequencing the transcripts of individual immune cells from human kidney biopsies. This multiomic approach allows authors to trace single nucleotide variants in expressed genes back to their cellular origin. Authors in this study performed single-cell immune profiling to examine the transcriptomes of over 5000 macrophages and 3600 lymphocytes. The authors observed that macrophages and T-cells originating from recipients exhibited unique proinflammatory gene expression patterns. Notably, donor-derived cells were found to persist for years post-transplant. Insights gained from this research could pave the way for more precisely tailored immunosuppressive medications, enhancing organ transplant outcomes.
Most human genetic variations identified in GWAS are located in the genome's noncoding regions, such as introns, promoters, and enhancers. To understand the function of these variants, comprehensive epigenetic profiling is key to reveal gene regulation patterns. Next-Generation Sequencing (NGS)-based epigenetic methods, including chromatin immunoprecipitation (ChIP-Seq), assay for transposase-accessible chromatin (ATAC-Seq), and chromosome conformation capture techniques like Capture C and HiC, are instrumental in this process. Additionally, conserved DNA methylation patterns may serve as a novel category of biomarkers. Integrating methylation or other epigenetic data with genetic information through multiomic approaches allows for the exploration of complex pathways and disease mechanisms by linking various functional layers.
Authors in this Nature Communications paper aimed to identify the specific genes affected by genetic variants associated with bone mineral density (BMD), as identified by Genome-Wide Association Studies (GWAS). Traditional GWAS successfully uncovered genetic signals linked to BMD but fell short in precisely localizing the effector genes responsible for these traits. To overcome this, authors leveraged a combination of high-resolution Capture C and ATAC-seq techniques in human mesenchymal progenitor cell-derived osteoblasts, which allowed them to map physical and direct connections between candidate causal variants and potential target gene promoters located in open chromatin regions. They identified consistent contacts for approximately 17% of the 273 BMD loci under investigation, exemplified by the knockdown of two newly implicated genes, ING3 and EPDR1, which influenced mesenchymal progenitor fate, highlighting the method's potential in discovering targets for osteoporosis and other genetic diseases.
A research team studied the role of epigenetic markers in major depressive disorder (MDD). They investigated the link between DNA methylation variations and lifestyle-related depression risks, such as smoking and body mass index (BMI). The team examined the genome-wide CpG methylation patterns in 9,873 samples from the Generation Scotland: Scottish Family Health Study. Their findings revealed that a "methylation risk score" significantly improved the prediction of depression when combined with genetic risk scores. This methylation factor accounted for 1.75% of the variance in MDD cases and remained relevant even after adjusting for the lifestyle factors. The study highlights the potential of multiomic data-based risk scores in enhancing the prediction and possible prevention of MDD.
Epigenetics and transcriptomics provide complementary insights into cellular differentiation and response processes. By integrating epigenetic analyses with RNA-Seq methods, we can directly observe the relationship between gene regulation and expression, rather than inferring it. This combined approach aids in identifying key genes and unraveling the mechanisms behind notable phenotypes. Employing this comprehensive and unbiased multiomics strategy can lead to the discovery of new regulatory elements, which are essential for identifying biomarkers and developing therapeutic targets.
Authors in this Nature Communications paper examines cellular heterogeneity in the adult human kidney by integrating single nucleus transcriptomics (snRNA-Seq) and chromatin accessibility profiling (snATAC-Seq), to generate paired cell-type-specific profiles, providing a comprehensive understanding of functional heterogeneity in the kidney. By comparing the chromatin accessibility and transcriptional profiles of different cell types, authors identified a significant proportion of differentially accessible chromatin regions closely associated with differentially expressed genes. This approach implicates the role of NF-κB in promoting VCAM1 expression, which drives transitions between subpopulations of proximal tubule epithelial cells.
Connecting genetic variations to protein expression at the single-cell level allows us to uncover unique cell identities and subtle cellular states, thereby enhancing our ability to conduct more informed research in the realms of disease and therapeutic development. This approach is particularly valuable in cancer research, where deciphering the relationship between genotype and phenotype is crucial for understanding tumor evolution and the progression of diseases.
Authors in this Nature paper utilized single-cell multiomics to examine clonal evolution in 146 myeloid cancer samples from 123 patients. The team conducted simultaneous single-cell mutational analysis and cell-surface protein measurement on over 740,000 cells using targeted sequencing panels. This approach enabled them to intricately connect genotype and phenotype, providing detailed insights into the complexity of cancer cell clones and their impact on disease progression.
While established technologies like single-cell RNA sequencing can uncover new cell types and states within complex tissues, they often struggle to distinguish functionally distinct categories of immune cells that may be molecularly similar.
Authors in this Cell paper conduct an independent unsupervised analysis of RNA and protein data, highlighting both consistent cell classifications and some discrepancies. Specifically, the paper mentions that CD8+ and CD4+ T cells were somewhat mixed when analyzing RNA but clearly separated in the protein data. On the other hand, conventional dendritic cells (cDCs), along with a rare population of erythroid progenitors and murine 3T3 controls, formed distinct clusters in RNA analysis but were mixed with other cell types based on protein abundance.