In my previous blog, I highlighted the uniqueness of single cell RNA sequencing technologies and how these can be used to understand 5’ and 3’ gene expression, T and B cell immune repertoire profiles, and more specific antibody-based approaches such as CITE-Seq as well as epigenetics approaches with ATAC-Seq. In this blog, the power of multi-omic approaches to simultaneously determine open chromatin regions with gene expression in a single cell is reviewed.

The scientific curiosity to understand the cause of a disease has led to many technological innovations. As the cost of genomic sequencing started to fall a decade ago, it opened up numerous new technologies that could provide unique insights in understanding disease biology even at a molecular level. These include whole genome data (genomics), changes in the structure of chromatin, understanding RNA sequences and their expression (transcriptomics) to proteomics-based approaches to understand protein structure, folding and the measurement of various metabolites (metabolomics). These broad array of technological advancements have helped in deciphering causal factors thus enhancing our ability to study and insight into many diseases through different dimensions and resolutions which were not previously possible.

A fundamental challenge in biomedical research is to identify accurate, early indicators of a disease. Recent advances in sequencing technologies have led to unparalleled efforts to characterize the molecular changes that underlie the development and progression of complex human diseases, including cancer. Scientists have widely used RNA-seq analysis to study the transcriptome in populations of cells. More recently, single-cell RNA seq studies have been used to gain insight on cellular traits and changes in cellular state.

Single-cell genomics techniques are revolutionizing our ability to characterize complex tissues. Although bulk RNA sequencing experiments can be insightful, they often mask important biological activity of rare cell types and fail to show the variability in gene expression between individual cells. The rapid development of low-input RNA seq methods has led to an explosion of single-cell RNA-seq platforms, each with their own advantages and limitations. Droplet-based methods (10X Chromium, DropSeq) can be used to analyze thousands of cells in a single prep. In this method, single cells are separated using microfluidics.

Technological advances in sequencing capabilities have rapidly accelerated our understanding of human health and disease. From the workhorse short-read Illumina sequencing data to the recent advent of third-generation sequencing instruments such as PacBio, Nanopore, that now enable single molecule sequencing, genomics and its applications has assumed a wider scope in recent times ranging from specialised studies such as transcriptomics, epigenomics, metagenomics to more specific application areas such as biomarker discovery, pharmacogenomics, disease diagnosis, identification of novel genes in disease mechanisms, drug repurposing and disease risk predictions.

Gall bladder cancer (GBC) is an aggressive gastrointestinal malignancy with a poor prognosis. It is the 20th most common type of cancer worldwide and its incidence is particularly high in specific regions of the world including Bolivia, Chile, Ecuador, Peru, Korea, Japan and India and is currently rising in Western populations (https://bit.ly/3kSLDMw) (Figure 1). In the United States, it is a more common malignancy in Southwestern Native Americans and Mexican Americans. There is also a gender disparity with GBC more prevalent in females than males.

In 2013, single-cell sequencing was selected as the method of the year to highlight its ability to sequence DNA and RNA in individual cells. The advantages of such high-resolution sequencing are to unveil previously unknown cell population heterogeneity and to perform more accurate analysis. The high degree of heterogeneity in tumor tissue is widely considered to relate to the mechanisms of tumorigenesis and metastasis. Traditional sequencing methods can only detect cell populations then get the average of the signals in a group of cells. Now with the help of single-cell sequencing and analysis, researchers can explore the heterogeneity among individual cells, detect rare cell types, study immune process and do much more.

Big data and big data analytics are the buzz words that we have been hearing for the past few years, which have relevance in all fields and specialties. In the field of medicine, the process of clinical documentation and analysis have been very meticulous and exhaustive in the past contributing to major discoveries in associating diseases with genes, understanding disease epidemiology and in generating and testing hypothesis. The advances in computational science and data processing have streamlined the management of medical big data creating opportunities to impact the health care system with accurate prognostication and disease management.

COVID-19 pandemic has infected over 23 million individuals and claimed over 800,000 lives globally as of August 2020. SARS-CoV-2, the organism causing COVID-19 belongs to the family of coronaviruses and shares 79% genome sequence identity with SARS-CoV. The spike antigen used by the virus to enter host cells became the prime target for immediate vaccine development efforts because of prior work on SARS-CoV that showed neutralizing antibodies against the spike antigen protected mice and chimps against new infection.

Cancer immunologists scooped the 2018 medicine noble prize for pioneering treatments that unleash the body’s own immune system to attack cancer cells. It represents a completely new principle which unlike the previous strategies that target the cancer cells, rather targets the brakes — the checkpoints — of the host immune system.