The analysis of individual cells promises to reveal insights into tissue physiology and disease that remain hidden in the study of bulk cell populations. By profiling single cells it is possible to search for rare cell sub- populations with distinct characteristics, such as drug-resistant cancer cells, which may have profound roles in health and disease. Single cell analysis can also shed light on how cell behavior is controlled, by testing for correlations between the activity of cell signaling, metabolism, cell cycle and cell differentiation within a tissue. To deliver on the promise of single cell analysis, it is necessaryto satisfy at least four technical requirements. The analysis must be carried out on a large number of cells;it should capture multiple measurements to construct a "cellular profile" or mRNA levels, protein levels, and so on;it should be sufficiently sensitive to detect changes in the profile between different cells;and it should be performed in tissues, or with cells immediately removed from tissues, to ensure that the measurements directly reflect the clinical situation. The problem of scale-up to large numbers of cells is particularly urgent. For example, if drug-resistant cancer cells constitute only 1% of a tumor, then to find even ten such cells requires analyzing 1,000 cells in total. Yet today there is a trade-off between analyzing many cells, and generating a wide cellular profile per cell. Methods that analyze many cells, for example Fluorescent Activated Cell Sorting (FACS), are restricted to a limited number of components and may therefore fail to identify rare cells of interest. Methods that can provide a more comprehensive profile are costly and labor-intensive, and therefore cannot be used on large numbers of cells. An ideal method should generate a cellular profile for <$1 per cell. We propose a method to simultaneously profile over 1,000 cells per run, using widely available sequencing technology. The method combines and adapts a number of existing molecular techniques to measure tens of proteins and 100-200 mRNA levels simultaneously in single cells. This breadth of measurement, particularly of protein levels, is not possible to achieve by any existing system at the single cell level. The method should also be more accurate than the closest comparable technology today, as it requires significantly less signal amplification. The long-term potential of the method is limited primarily by the capabilities of high-throughput sequencing technology, and as sequencing technology continues to improve dramatically in the race to deliver a $1,000 human genome, the cost-efficiency, speed and cell throughput of the method are also expected to improve. We project that this method will eventually cost significantly less than $1 per cell profile. Thus, if successful, this method would provide a profound step forward for single cell analysis.
A critical limitation facing single cell analysis is the current trade-off between analyzing many cells and many cellular components. We propose a method that overcomes this limitation by profiling the levels of 100-200 mRNA transcripts and tens of proteins in 1,000 or more cells at reasonable cost (~$1/cell) and with few steps. The method combines several established assays, and introduces a novel combinatorial bar-coding strategy to simultaneously read out the profile of all cells using next-generation sequencing.