Reizis, B. | bioRxiv Science

bioRxiv2022

Genome-wide analysis of dendritic cell differentiation

Dendritic cells (DCs) are immune sentinel cells that comprise antigen-presenting conventional DCs (cDCs) and cytokine-producing plasmacytoid DCs (pDCs). Cytokine Flt3 ligand (Flt3L) supports the proliferation of hematopoietic progenitors, and is also necessary and sufficient for DC differentiation. Here we characterized the spontaneous differentiation of a Flt3L-dependent murine progenitor cell line into pDCs and "myeloid" cDCs (cDC2s), and interrogated it using a genome-wide CRISPR/Cas9 dropout screen. The screen revealed multiple regulators of DC differentiation including the glycosylphosphatidylinositol transamidase complex, the Nieman-Pick type C cholesterol transporter and arginine methyltransferase Carm1; the role of Carm1 in pDC and cDC2 differentiation was confirmed by conditional targeting in vivo. We also found that negative regulators of mTOR signaling, including the subunits of TSC and GATOR1 complexes, restricted progenitor growth but enabled DC differentiation. The results provide a comprehensive forward genetic analysis of DC differentiation, and help explain how the opposing processes of proliferation and differentiation could be driven by the same cytokine.

immunology↗

bioRxiv2020

iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis

Under-sampling RNA molecules and low-coverage sequencing in some single cell sequencing technologies introduce zero counts (also known as drop-outs) into the expression matrices. This issue may complicate the processes of dimensionality reduction and clustering, often forcing distinct cell types to falsely resemble one another, while eliminating subtle, but important differences. Considering the wide range in drop-out rates from different sequencing technologies, it can also affect the analysis at the time of batch/sample alignment and other downstream analyses. Therefore, generating an additional harmonized gene expression matrix is important. To address this, we introduce two separate batch alignment methods: Combined Coverage Correction Alignment (CCCA) and Combined Principal Component Alignment (CPCA). The first method uses a coverage correction approach (analogous to imputation) in a combined or joint fashion between multiple samples for batch alignment, while also correcting for drop-outs in a harmonious way. The second method (CPCA) skips the coverage correction step and uses k nearest neighbors (KNN) for aligning the PCs from the nearest neighboring cells in multiple samples. Our results of nine scRNA-seq PBMC samples from different batches and technologies shows the effectiveness of both these methods. All of our algorithms are implemented in R, deposited into CRAN, and available in the iCellR package.

bioinformatics↗