We developed a pseudotargeted metabolomics method, ion-pairs used for MRM are extracted from untargeted mass spectrometry data by using mixtures of samples to be analyzed, semi-quantitative information of each sample is obtained by the MRM without identification of the metabolites.
We developed CARPID method to identify binding proteins of specific lncRNAs in the native cellular context, which jointly leverages enzymatically inactive CRISPR/dCasRx-based RNA targeting and proximity biotin-labeling.
We describe SCCAF a computational approach to identify putative cell clusters from single-cell RNA-seq data. SCCAF automatically identifies the “ground truth” cell assignments with high accuracy in various benchmark datasets and captures the discriminative feature genes of the cell types.
We modified the commercially available microraft array technology with standard confocal imaging techniques to perform an imaging-based CRISPR screen for regulators of a protein localization phenotype.
We describe a kethoxal-assisted single-stranded DNA sequencing (KAS-seq) approach. KAS-seq allows rapid (within 5 min), sensitive and genome-wide capture and mapping of ssDNA produced by transcriptionally active RNA polymerases or other processes in situ using as few as 1,000 cells.
Oligomerisation of membrane proteins remains a field characterized by intense research interest, due to the large number of pharmacological and clinical implications that the formation of molecular complexes of signaling proteins carries.
We present Spatial PAttern Recognition via Kernels (SPARK) as an effective statistical tool to identify genes with spatial expression patterns in spatially resolved transcriptomic studies. A particular feature of SPARK is its ability to produce calibrated p-values for spatial analysis.