UCLA

3’ RNA sequencing provides an alternative to whole transcript analysis. However, we do not know a priori the relative advantage of each method. Thus, a comprehensive comparison between the whole transcript and the 3’ method is needed to determine their relative merits. Single cell RNA sequencing (scRNA-seq) enables the profiling of the transcriptomes of individual cells. Cell type identification is one of the major goals in scRNA-seq. Current methods for assigning cell types have several limitations, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Thus, new methods need to be developed. We first used two commercially available library preparation kits, the KAPA Stranded mRNA-seq kit (traditional method) and the Lexogen QuantSeq 3’ mRNA-seq kit (3’ method), to determine the advantages and disadvantages of these two approaches. We found that the 3’ RNA-seq method detected more short transcripts than the whole transcript method. With regard to differential expression analysis, we found that the whole transcript method detected more differentially expressed genes, regardless of the level of sequencing depth. We then developed ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network to predicts cell types for scRNA-seq datasets. We trained and tested ACTINN on multiple datasets, the results showed that ACTINN is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Lastly, we performed scRNA-seq to study gene networks associated with host defense comparing lesions from reversal reaction vs. lepromatous lesions from leprosy patients. We constructed an antimicrobial ecosystem by integrating the IFNG and IL1B antimicrobial targets with the cell-cell co-abundance in lesions, which revealed that the interaction of dendritic cells, macrophages, T cells, keratinocytes and fibroblasts contributes to the capacity of granulomas to eliminate the pathogen in leprosy.