Studying the Genome One Cell at a Time

In 2013, Nature Publishing Group named single-cell sequencing (SCS) as the ‘Method of the Year’, citing that the ability to sequence the DNA and RNA of single cells will transform the fields of biology and medicine. By that time, the first genome-wide single-cell RNA and DNA sequencing methods for mammalian cells had already been introduced and the field of genomics was experiencing a major paradigm shift: scientists were stepping away from bulk tissue analysis and moving towards an approach that looked at one cell at a time.

A mere five years later, the technique has advanced in leaps and bounds, bringing to light how genetic information can change between individual, seemingly identical, cells within a population, and facilitating the study of rare cell types. These insights into cellular heterogeneity have helped scientists understand how diseases like cancer can evolve and spread in humans. 


Single-Cell Sequencing in Multiple ‘Omes’

Professor Xinghua (Victor) Pan, chairman of the Department of Biochemistry and Molecular Biology in the Southern Medical University, Guangzhou, China, and director of the Guangdong Provincial Key Laboratory of Single-Cell Technology and Application, talked to us about why sequencing single cells holds enormous promise not just in genomics, but in multiple ‘omics’ fields i.e. transcriptomics and epigenomics.

“SCS is applied every day in cancer research, as well as in the interrogation of stem cells, neurons, and immune cells, where high cellular heterogeneity is involved,” said Pan. “This technique allows us to deconstruct the complexity of cell populations with extremely high resolution, from constitution and molecular basis to dynamic processes.”

The application of SCS can be broadly broken down into three areas:

1. Single cell genomics: 

                a. single-cell whole-genome sequencing (scWGS)

                b. single-cell whole exome sequencing (scWES)

2. Single cell transcriptomics: Single-cell RNA sequencing (scRNA-seq), 

3. Single cell epigenomics: A variety of techniques including -

                a. single-cell DNA methylation sequencing,

                b. single-cell chromatin accessibility sequencing (scATAC-seq), 

                c. single-cell chromatin conformation capture (sc3C-seq), etc.

Pan and his colleagues have explored many of the above techniques over the years and come to know exactly how and when to use each type of analysis in their cell biology research. Recently, Pan and collaborators turned to exome sequencing in an effort to tease out new mutations in colorectal cancer (CRC) and clarify its evolution and molecular mechanism.  Using, scWES, they showed that both CRC and non-cancerous cell masses in the colon (adenomas) can in fact originate from the same clone, but branch out into different subclones with heterogeneous mutations in a variety of signaling pathways, ultimately leading to either the cancer or benign growths.

“The exome represents all the exons, which carry coding information for proteins, in the whole genome. The mutations that pop up in the exome are very important to understand how a cancer develops and spreads,” Pan said, taking us through the reasoning behind applying scWES to his work. “Due to the heterogeneity of the cancer cells, bulk-cell exome sequencing gives us sort of an average of the ‘message’ tied to the mutations leading to cancer. This average hides the actual constitution and the dynamic trajectory of the cell system (the cancer), which can usually be found in different sub-populations of cells.”

Pan emphasized that scWES elucidates the significance of specific mutations in specific pathways which might not be as apparent from bulk sequencing of tumors. His team used scWES to establish whether these mutations are mutually exclusive or have a pattern of occurring in the same subclone of cells. “Only certain sub-populations may be the key for cancer progress and drug resistance, and they should be exploited for diagnosis and treatment.”

Heterogeneity in Ovarian Cancer

Halfway across the world from Pan’s lab, another team of researchers using scWES and scRNA-seq have made significant headway in uncovering the heterogeneous mysteries of ovarian cancer. Professors Boris Winterhoff and Tim Starr of the Dept. of Obstetrics, Gynecology and Women's Health, University of Minnesota Medical School, Minnesota, USA, decided to sequence single tumor cells from a patient with high-grade serous ovarian cancer (HGSOC) in the hopes of better understanding mechanisms of treatment resistance. Their focus was on BRCA1, a tumor suppressor gene whose mutations cause up to 20% of ovarian cancers in women. 

“Patients with BRCA1 tumors can have reversion mutations that could contribute to their disease recurrence and progression,” said Winterhoff. “By sequencing this patient’s tumor, we wanted to examine and compare her BRCA1 locus between individual tumor cells.”

Winterhoff’s group started this study as part of a larger precision medicine initiative that analyzes freshly resected tumor to identify new targets for ovarian cancer treatment.  Targeted therapies like PARP-inhibitors have shown benefits in patients resistant to first-line therapies à la radio- and chemotherapy. Some patients go into remission for years; however, others only show a limited window of response before disease progression. “Through analysis of heterogeneity within the BRCA1 locus, we can potentially identify certain patients who may already have BRCA1 reversions or other polymorphisms up front which can help predict their ultimate response to targeted therapy.”

Starr agrees with Pan that sequencing data from other bulk next-gen methods can create an aggregate expression fingerprint from the whole cell population, but the subtle differences between each tumor cell are not accurately represented. “To analyze specific changes in the genome that led to malignancy, we had to analyze the tumor exome via a single-cell sequencing platform,” Starr explained. “We are currently analyzing other clinically actionable genes beyond BRCA1 as well as using scRNA-seq and scWES to analyze a larger cohort of patients.”

Sequencing Creeping Fat

It’s a well-known fact that diet can influence the gut microbiome both positively, and negatively. These changes can lead to the development of inflammatory bowel diseases, one of which is Crohn’s Disease, studied extensively by Dr. Suzanne Devkota, director of Microbiome Research at Cedars-Sinai Medical Center, California, USA. Her recent abstract discusses the use of scRNA-seq to explore cellular changes in ‘Creeping Fat’ associated with Crohn’s.

“Creeping fat is the mesenteric fat attached to the outside of the small intestines,” said Devkota. “At sites of severe inflammation, this fatty tissue will expand and wrap around the small bowel. Because no one has ever described the cellular composition and gene expression of creeping fat, we felt the only way to do this definitively was through scRNA-seq.”

After multiple failed attempts to determine cell types and phenotypes via flow cytometry and classical adipose markers, Devkota’s team realized that creeping fat was actually more complex and heterogeneous in structure than they initially thought. “In our opinion, the only way we could tackle this complexity was by scRNA-seq.” Devkota admits that the SCS technique is still relatively young, but evolving very rapidly, especially in terms of bioinformatics. “The enormity of the single-cell datasets and insights obtained blow previous RNA-seq methods out of the water.”

The SCS data from the creeping fat study uncovered interesting microbial and host signatures that may have diagnostic and therapeutic potential. They found that the cellular composition of creeping fat was indeed different from adjacent healthy mesenteric fat. “What’s really intriguing is that, the gene profiles of these cells tell a story of how live bacterial movement in the gut can cause the cells to respond and drive the formation of creeping fat,” remarked Devkota.

The Future of Single-Cell RNA Sequencing

Although scRNA-seq has come a long way in a short amount of time, we are only beginning to discover the ultimate potential of the technique. For example, it has not yet been adequately applied to microbiome research, as microbiomes present a much more challenging sample for scRNA analysis, due to the cell wall and non-polyadenylated RNA. Devkota believes that, eventually, as the platforms evolve, researchers will be able to perform scRNA-seq on encapsulated bacteria.

Most industry experts agree that over the last five years, SCS has largely overcome sample prep bottlenecks. The industry today consists of technologies with innovative partitioning solutions, including droplets and cell barcoding strategies, that eliminate problems associated with throughput and high cost per cell.

It is apparent that at the rate SCS is advancing, we will soon see improvements in the application space (including a variety of challenging sample types), the technology itself (automation, scalability, cost, sensitivity, etc.), and bioinformatics. “I decided to make a major investment in SCS in my lab because I believe it is the future, and there’s currently no technology that can provide this level of insight,” Devkota enthused.

Getting back to his cancer research work in the Guangdong Lab, Pan concluded our conversation by pointing out that “SCS alone cannot give us everything. It is a very special new frontier, but we have to combine it with other techniques, in order to avoid bias and really understand the complex mechanistic and molecular mysteries in life science and medicine.”