Research Services Blog

Head and neck squamous cell carcinoma (HNSCC) ranks among the most prevalent cancers worldwide. In the United States, it is estimated that 58,450 new cases will be diagnosed in 2024, primarily affecting the oral cavity and pharynx1. Incidence rates among males are highest in non-Hispanic White and American Indian/Alaska Native individuals, with lower rates observed in Hispanic and Asian/Pacific Islander populations. Among females, incidence rates are elevated in non-Hispanic White and Asian/Pacific Islander individuals, while being lowest in Hispanic and Black populations.

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.

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.

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

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.

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.

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.

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.

Spatial transcriptomics is a technology that allows the analysis of gene expression patterns within a tissue sample in their spatial context. It enables researchers to obtain a comprehensive and high-resolution view of the transcriptome, the set of all expressed genes, across different regions of the tissue. In traditional transcriptomics, gene expression is measured from homogenized cell populations, which can mask important differences in gene expression between different cell types and regions. Spatial transcriptomics, on the other hand, allows researchers to analyze gene expression patterns in intact tissue sections while retaining their spatial information.

Next-generation sequencing (NGS) data is being increasingly used in clinical diagnosis to identify genetic variation that can be a cause for the disease. A major challenge in using NGS data in a clinical setting is to make the right interpretation because of its huge size and complexity. Also, there are possibilities of technical errors during the sample processing and/or sequencing stage that may be inherent to the kind of sequencing technology used. Therefore, the use of reference standards is of paramount importance to mitigate and minimize these errors.

NGS technologies is at the forefront of Biological Research. They produce enormous data running into gigabases in a single round of sequencing. However, several sequencing artifacts such as read errors (base calling errors and small insertions/deletions), poor quality reads and primer/adaptor contamination are quite common with the NGS data obtained after sequencing.

Our journey in 2022 was focused on providing the utmost customer experience for the services and solutions that we delivered to you. Along with expanding our portfolio of services and solutions – the tissue dissociation and nuclei isolation services to support our single cell customers, streamlined antibody discovery using high-throughput single B cell receptor sequencing, TSO500 targeted panels for oncology research, single cell and bulk epigenetics assays.

The discovery of genetic and epigenetic mechanisms underlying the onset and progression of numerous diseases, including cancer, has helped redefine clinical research, diagnostic and treatment paradigms. Oncology research and diagnostics have undergone radical changes because of the development of next-generation sequencing (NGS). NGS has improved rationally designed personalized cancer medicine by identifying novel cancer mutations, detecting circulating tumor DNA (ctDNA), and discovering causative mutations for hereditary cancer syndrome. With NGS, it is now possible to sequence the whole genome, whole exome, whole transcriptome, or just targeted genes to provide detailed genomic landscape descriptions for many cancers.

Alzheimer’s disease (AD) has long been one of the great challenges in medicine and imposes a constant burden on our aging population. Recent statistics show that approximately 50 million people worldwide suffer from AD or some other form of dementia. The World Health Organization has estimated that the total number of people with dementia worldwide will reach 82 million by 2030 and 152 million by 2050. Of the top 10 leading causes of death based on United States cancer statistics, cardiovascular disease ranks first, tumors rank second and AD ranks sixth.

Modern medicine now derives its insights through the deeper understanding of the cellular and molecular mechanisms, which involves modification of the cellular behavior through targeted molecular approaches. Experimental biologists and clinicians now employ various molecular techniques to assess the intrinsic behavior of cells in a variety of ways, such as through analyses of genomic DNA sequences, chromatin structure, messenger RNA (mRNA) sequences, non-protein-coding RNA, protein expression, protein modifications and metabolites.

Emerging single-cell technologies have provided us with a powerful tool to dissect the clonal complexity of tumor cells, deconvolute the role of immune cell types in disease mechanisms, and monitor risk and treatment strategies to guide early patient diagnosis, since being highlighted as the ‘method of the year’ in 2013. As our capabilities in single cell sequencing continue to increase, latest advances in multi-omics of single cells are providing newer ways of integrating single cell transcriptomics with the multiple molecular measurements in a single experiment.

Recent advances in next-generation sequencing technologies have heralded a paradigm shift in the field of precision oncology and personalized/genomic medicine, with a large number of somatic- and germline mutation-profiling programs worldwide. These programs have paved the way for personalized medicine in contrast to a unified approach that clearly fails in select individuals, conferring benefits to only a subset of patients. While these genomic analyses become increasingly accessible and almost commonplace to all research scientists, clinicians and molecular geneticists, they are faced with the challenging task of interpreting and translating the results from these analyses.