Project description:We developed a new method on sequencing low-input RNA. This method shows much low-bias with the advantage of semiconductor while competing with smart-seq2. In order to analyze the low-input RNA datasets sensitively, we also develop FANSe2splice with high experimental verification rate as the analysis tool in our method.
Project description:Single-cell RNA -seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA -seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well extensively and use it to profile thousands of primary human macrophages exposed to tuberculosis.
