Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis
A cell fate change such as tumorigenesis incurs critical transition. It remains a longstanding challenge whether we can unravel the underlying mechanism and find a molecular switch that can reverse such transition. Here we present a systems framework, REVERT with which we can reconstruct the core molecular regulatory network model and identify a reversion switch based on single-cell transcriptome data over the transition process. We demonstrate the usefulness of REVERT by applying it to single-cell transcriptome of patient-derived matched organoids of colon cancer and normal colon. REVERT is a generic framework that can be applied to investigate various cell fate transition phenomena. Overall design: Establishing and preprocessing single-cell RNA sequencing libraries from dissociated organoids using 10x genomics: Organoids were dissociated into single cells for RNA sequencing analysis. Following rinsing with phosphate-buffered saline (PBS), organoids were enzymatically dissociated using TrypLE Express (Gibco) and incubated at 37°C for 15 minutes. During incubation, the mixture was pipetted every 5 minutes to promote complete dissociation. The enzymatic reaction was halted by adding Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS). The resultant cell suspension was filtered through a 40 µm cell strainer to achieve a uniform single-cell suspension. Cell viability and density were assessed using Trypan Blue exclusion with a hemocytometer to ensure optimal cell concentration for sequencing. For library preparation, the single-cell RNA sequencing library was constructed using the Chromium Single Cell Instrument (10x Genomics), following the manufacturer's protocol. The process involved loading cells into a Chromium Chip B where cells, master mix, and partitioning oil were combined to generate single-cell gel beads in emulsion (GEMs). Post GEM-RT reaction, GEMs were broken, and the barcoded cDNA was isolated, cleaned using DynaBeads MyOne Silane Beads (Invitrogen), and amplified by PCR. The quality and quantity of the amplified cDNA libraries were evaluated using an Agilent 4200 Tapestation system (Agilent Technologies). The libraries were sequenced on an Illumina Nova-seq 6000 system, employing paired-end sequencing as recommended by the manufacturer. This method ensures the generation of high-quality data for transcriptomic analysis. In addition, we excluded apoptotic cells that express more than 15% mitochondrial transcripts, as these are considered low-quality cells. Following this filtering step, 18049 cells were retained for further analysis. Each cell underwent log normalization by scaling to a constant total read count per cell (100,000), followed by log transformation. For visualization, principal component analysis (PCA) was initially conducted using 2,000 highly variable genes for dimensionality reduction. Subsequently, PCA dimensions were utilized to project into 2D space. All these analyses were conducted using the Seurat v4.0.2 package in R (v4.2.0).