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. It can impose significant impact on the downstream analysis such as sequence assembly, single nucleotide polymorphisms (SNP) identification and gene expression studies.

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. We improved our turn-around times on bulk transcriptomics, whole exome and whole genome projects by incorporating automation at multiple project stages and installing new sequencing capacity in 2022. Our sequencing team has delivered high-quality data consistently for a variety of library types throughout the year.

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.

According to the American Cancer Society, an estimated 1.9 million new cancers will be diagnosed in 2022 [1]. Some of the major cancer types affecting the population are prostate, lung & bronchus, colon & rectum, urinary bladder, melanoma of the skin, kidney & renal pelvis, non-Hodgkin lymphoma, oral cavity & pharynx, leukemia, pancreas, breast, colon & rectum, uterine corpus, thyroid. Lung and Bronchus (21%) in both men and women, prostate in men (11%) and breast cancer (31%) in women are the majority cancer types causing death in the population [1]. Even though our understanding of cancer has broadened over the years it is still a major challenge to tackle across the globe. Widely accepted therapy forms for cancer includes biomarker identification and testing for treatment, chemotherapy, hormone therapy, immunotherapy, photodynamic therapy, radiation therapy, stem cell transplant, surgery, and targeted therapy. Immunotherapy (Table 1) is emerging as a forerunner among all the types of cancer therapies for the simple reason as it considers the various dynamics of immune function in an individual. Genomics has played a key role in enabling the identification of therapeutically actionable targets and in guiding the use of immunotherapy.