bioinformatic analysis matlab script  (MathWorks Inc)

Journal: STAR Protocols

Article Title: Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling

doi: 10.1016/j.xpro.2021.100673

Figure Lengend Snippet: Expected outcomes for each step

Article Snippet: The computational pipeline consists of 1. the bioinformatic analysis MATLAB script and 2. two fastq files (read 1 and read 2) of locus specific amplicon libraries 2.

Techniques: Magnetic Beads, Cell Counting, Nested PCR, Mutagenesis, cDNA Library Assay

Journal: STAR Protocols

Article Title: Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling

doi: 10.1016/j.xpro.2021.100673

Figure Lengend Snippet: Accurate identification of the mutated cells from the amplicon libraries (A–C) (A) In a control experiment MOLT4 (WT cells) were mixed with UKE-1 cells (homozygous JAK2-V617F mutation) and ran through the experimental and analysis pipeline. The two cell populations could be distinguished based on their transcriptional profiles: two distinct clusters were seen when transcriptomes of the cells were visualized using UMAP. Marker genes (TCF7 shown here) were used to identify the clusters as either MOLT4 or UKE-1 cells. Cells in which a mutated JAK2 transcript (B) or a WT JAK2 transcript (C) were detected in the amplicon libraries are shown as colored points. All other cells are shown in gray. (D) All cells in which either a WT or mutated JAK2 transcript was detected in the amplicon libraries. JAK2 transcript were detected in ~ 4% of cells (249 out of 6563 cells). The rate of erroneously detecting a mutated transcript in a MOLT4 cell or a wildtype transcript in a UKE-1 cell in less than 1%. (E–G) Output plots from MATLAB analysis script. (E) Rank of unique indices. Index sequence can be found in MATLAB cell array ‘uniqueindices’. (F) Number of reads vs rank of unqiue molecules and the threshold for calling the detected molecules as either wildtype or mutated. (G) Number of reads vs rank of unique cells. (H) Example of top 200 most common Read 2 and its align results. Related to Figure 1

Article Snippet: The computational pipeline consists of 1. the bioinformatic analysis MATLAB script and 2. two fastq files (read 1 and read 2) of locus specific amplicon libraries 2.

Techniques: Amplification, Control, Mutagenesis, Marker, Sequencing