Often in the development of cancer the impact of disease progression and developmental biology is lost when performing single-cell analysis due to the limitations of the assay. CRISPR Array Repair LINeage tracing (CARLIN) is a mouse line that offers genetic barcoding for cell lineage tracing capabilities that can provide resolution on this issue.This technology utilizes CRISPR technology to generate up to 44,000 barcodes that can be transcribed when induced by doxycycline. Doxycycline is used to induce cas9 activation which leads to double strand breaks in a target array. These double strand breaks are repaired by non-homologous end joining (NHEJ), which is an error prone DNA repair process, to become altered sets of heritable and transcribable DNA, referred to in the study as CARLIN alleles.
Tracking Progenitor Cells
When paired with advancements in single-cell transcriptomics, CARLIN technology has allowed researchers to follow progenitor cells down lineages of cell differentiation. During the development of CARLIN, the authors follow hematopoietic stem cells (HSC) from progenitors to adulthood. After performing single-cell analysis on four bone tissues from the mice, researchers were able to find 46 clones in the HSC cell clusters. Interestingly, 13 of these 46 clonal lineages made up 46% of all HSC cells found in the study. Even more so, the study was able to find that descendants of these clones were not equally distributed across bone tissue, implying that certain clones had contributed to specific cell populations more than others. This can be a vital insight into understanding the molecular biology that drives diseases where the developmental biology of HSCs in embryos is not fully resolved. In addition, the pilot study made an important technical finding early in analysis that CARLIN offers a sufficient alternative to finding cell signatures without cell sorting.
A recent study has applied this technology to study the metastasis of tumor cells. By tracing cell lineage with inherited barcodes, researchers were able to track which tumor cells were most likely to give rise to tumors in other areas of the body. Paired with single cell transcriptomics, we can then begin to understand what pathways may promote metastasis in specific lineages. This study was able to find specific clonal lineages that were at higher risk of metastasis than others shown by closely related cell lineages being found in multiple tissues. Transcriptomic analysis of these tissues allowed researchers to identify 48 genes that are associated with increased metastatic risk. Inversely, in lineages that were not found to be present in multiple tissues, the paper finds 44 genes associated with lower risk of metastasis. A clear future application recommended by the investigators of this study is to devise an experiment to test which cancer lineages evolve drug resistance.
The Future of Tumor Oncology and Precision Diagnostics
Being able to tie modern transcriptomics to cell lineage tracing gives hope to address complex questions in the tumor oncology space. One issue that comes to mind is that often when a tumor patient relapses, it is the result of “pre-cancerous” cells matriculating into cancer cells. The ability to trace cell lineage and tie that to transcriptomics allows scientists to begin to assess the risk and mechanism of disease of relapse in tumor oncology studies.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
Groundbreaking studies like these are made possible by technological advances making biological data generation, storage and analysis faster and more accessible than ever before. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey.
As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.
Josh Stolz, Data Scientist
Josh, Data Scientist & Content Writer for Bridge Informatics, most enjoys work that sits at the intersection of complex molecular biology and genomics. He is an expert at identifying biomarkers, developing statistical methods, and next generation sequencing.
Before joining BI, Josh worked at the Johns Hopkins Lieber Institute of Brain Development where he used RNA-seq data to illuminate the underlying causes of schizophrenia. He also worked at Abbvie where he used genomic technologies to bolster clinical trial portfolios surrounding eye related treatments.
Josh received a BS in Biology from Indiana State University and an M.Sc in Bioinformatics. If he’s away from his desk, you will likely find Josh running along the Baltimore harbor.