Red-Horse, K. — Page 2 | Search bioRxiv

bioRxiv2019

CellGPS: Whole-body tracking of single cells by positron emission tomography

In vivo molecular imaging tools are critically important for determining the role played by cell trafficking in biological processes and cellular therapies. However, existing tools measure average cell behavior and not the kinetics and migration routes of individual cells inside the body. Furthermore, efflux and non-specific accumulation of contrast agents are confounding factors, leading to inaccurate estimation of cell distribution in vivo. In view of these challenges, we report the development of a \"cellular GPS\" capable of tracking single cells inside living subjects with exquisite sensitivity. We use mesoporous silica nanoparticles (MSN) to concentrate 68Ga radioisotope into live cells and inject these cells into live mice. From the pattern of annihilation photons detected by positron emission tomography (PET), we infer, in real time, the position of individual cells with respect to anatomical landmarks derived from X-ray computed tomography (CT). To demonstrate this technique, a single human breast cancer cell was tracked in a mouse model of experimental metastasis. The cell arrested in the lungs 2-3 seconds after tail-vein injection. Its average velocity was estimated at around 50 mm/s, consistent with blood flow rate. Other cells were tracked after injection through other routes, but no motion was detected within 10 min of acquisition. Single-cell tracking could be applied to determine the kinetics of cell trafficking and arrest during the earliest phase of the metastatic cascade, the trafficking of immune cells during cancer immunotherapy, or the distribution of cells after transplantation in regenerative medicine.

bioengineering↗

bioRxiv2017

Index Switching Causes "Spreading-Of-Signal" Among Multiplexed Samples In Illumina HiSeq 4000 DNA Sequencing

Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output per run at a fraction of the time and cost of conventional technologies. The process typically involves four basic steps: library preparation, cluster generation, sequencing, and data analysis. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines (HiSeq 3000/4000/X Ten) called exclusion amplification (ExAmp), which was a fundamental shift from the earlier method of random cluster generation by bridge amplification on a non-patterned flow cell. The ExAmp chemistry, in conjunction with patterned flow cells containing nanowells at fixed locations, increases cluster density on the flow cell, thereby reducing the cost per run. It also increases sequence read quality, especially for longer read lengths (up to 150 base pairs). This advance has been widely adopted for genome sequencing because greater sequencing depth can be achieved for lower cost without compromising the quality of longer reads. We show that this promising chemistry is problematic, however, when multiplexing samples. We discovered that up to 5-10% of sequencing reads (or signals) are incorrectly assigned from a given sample to other samples in a multiplexed pool. We provide evidence that this \"spreading-of-signals\" arises from low levels of free index primers present in the pool. These index primers can prime pooled library fragments at random via complementary 3 ends, and get extended by DNA polymerase, creating a new library molecule with a new index before binding to the patterned flow cell to generate a cluster for sequencing. This causes the resulting read from that cluster to be assigned to a different sample, causing the spread of signals within multiplexed samples. We show that low levels of free index primers persist after the most common library purification procedure recommended by Illumina, and that the amount of signal spreading among samples is proportional to the level of free index primer present in the library pool. This artifact causes homogenization and misclassification of cells in single cell RNA-seq experiments. Therefore, all data generated in this way must now be carefully re-examined to ensure that \"spreading-of-signals\" has not compromised data analysis and conclusions. Re-sequencing samples using an older technology that uses conventional bridge amplification for cluster generation, or improved library cleanup strategies to remove free index primers, can minimize or eliminate this signal spreading artifact.

molecular biology↗