Colorectal cancer (CRC) stands as the third most prevalent cancer and the second leading cause of cancer-related deaths in the US. It is projected that in 2024, there will be around 106,590 new cases of colon cancer and 46,220 new cases of rectal cancer. CRC incidence is notably higher among African Americans and lowest in Asian Americans/Pacific Islanders. The five-year relative survival rate for localized CRC is estimated to be 91%, contrasting with a 14% rate for metastatic disease. Mortality rates among older adults have seen a decline in recent years owing to factors such as the implementation of screening programs, advancements in imaging technology for precise staging, improvements in surgical procedures, and the development of new treatment modalities. Nevertheless, concerning trends reveal a 1% annual increase in CRC mortality rates among individuals under 55 since the mid-2000s.

National Cancer Prevention Month is observed in the month of February every year, with an objective to raise awareness and promote initiatives to prevent cancer. Cancer ranks as the second leading cause of death in the United States (US). Despite government-led cancer education initiatives, the battle against this disease remains complex, with variations in cancer risk persisting among different ethnic groups due to genetic predispositions and disparities in healthcare access. The incidence of different cancer types varies among population groups, influencing cancer rates within diverse demographics, often associated with genetic factors.

Single-cell RNA sequencing (scRNA-seq) is a powerful method that is widely used in biomedical research. It is extensively used to determine cell composition of complex tissues, identify rare cell types, map heterogeneity at single cell level and identify paired, full-length immunoglobulin sequence and T-cell receptor α/β. Advancements in high-throughput single-cell RNA sequencing technologies, in combination with powerful computational tools, has made scRNA-seq a widely used technology across a broad spectrum of therapeutic areas such as oncology, immunology, neuroscience and developmental biology. Requirement of live cells for most single cell workflows is a bottleneck that limits its wider usage. Advent of 10x genomics Flex protocol has enabled single cell gene expression profiling using fixed samples including FFPE samples. This offers several advantages compared to conventional single cell workflows.

Transcriptome sequencing/RNA sequencing allows unbiased characterization of global gene expression profiles associated with different cells/tissues. As genes govern cellular function, transcriptome profile can provide valuable insights into molecular mechanisms operating in a biospecimen. RNA sequencing has transformed biological research by discovering almost all transcripts encoded by a genome including mRNAs, long non-coding RNAs and miRNAs. It has also revealed many alternatively spliced variants which is a common feature among complex multicellular organisms. It has revolutionized biomedical research by enabling characterization of global gene expression profiles associated with cells/tissues in healthy and disease conditions. Understanding molecular mechanisms of disease has paved way for identification of biomarkers and development of novel drugs targeting specific genes that drive disease pathology.

The field of immune repertoire profiling has witnessed remarkable advancements in recent years, revolutionizing our understanding of the immune system and its role in various diseases. One of the key techniques to understand this complex mechanism is TCR sequencing. TCR, or T-cell receptor, plays a crucial role in the adaptive immune response by recognizing and binding to specific antigens. By sequencing the TCR repertoire, researchers can gain valuable insights into the diversity and specificity of T-cell populations, leading to the development of novel treatment modalities in infectious diseases, autoimmunity, and immuno-oncology.

Genetic variation can range from changes at the level of single bases to whole-chromosomal aneuploidies. Structural variations (SVs) refer to a large alterations in chromosomal structure, typically encompassing larger than 1 Kbp of DNA. SVs include both balanced changes, such as inversions and some forms of translocations, as well as those that alter DNA copy number through duplications and deletions of chromosomal segments. SVs account for 25% of protein truncating mutations and are 3 times more likely to associate with a genome-wide association study (GWAS) signal than single nucleotide variants (SNVs). SVs contribute to all classes of genetic disease: sporadic development syndromes, Mendelian diseases, complex disorders and infectious diseases, as well as health-related metabolic phenotypes.

Bioinformatics plays a vital role in analyzing complex high-throughput sequencing data, particularly in the realm of single cell research. The ability to analyze and interpret massive amounts of single cell data has revolutionized our understanding of cellular heterogeneity and its implications in various biological processes. The blog explores the capabilities of bioinformatics team at MedGenome in analyzing single cell sequencing data. Here, we explore different types of bioinformatics reports, the importance of data visualization and generation of interactive reports such as differential gene expression analysis, heatmap visualization, interactive tSNE plots with cell type and cluster information.

The advent of single cell sequencing technologies has enabled us to understand and study the complexities of biological systems at a finer resolution. Traditional bulk sequencing methods provide an average representation of gene expression across a population of cells, masking the inherent heterogeneity that exists within a tissue or organism. However, single cell sequencing allows us to capture the maximal transcript diversity in a given cell and allows for a multi-model analysis strategy to generate meaningful insights.

Cite-Seq, short for Cellular Indexing of Transcriptomes and Epitopes by sequencing, is a powerful technology that has revolutionized single-cell sequencing. With its ability to analyze transcriptomes and protein expression at a single-cell level, Cite-Seq has the potential to greatly advance our understanding of cellular heterogeneity and function in biological systems. In this article, we will discuss the workings of Cite-Seq, its current and potential applications in various fields of research, and its limitations.

Single cell sequencing is a cutting-edge technique used in molecular biology that enables the sequencing of the transcriptome of individual cells. In traditional bulk sequencing techniques, RNA is extracted from a large group of cells, and then sequenced as a whole. However, single cell sequencing allows researchers to analyze the genetic material of individual cells, providing a much more detailed and precise understanding of the diversity and heterogeneity of cell populations.