ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

CellRomeR: an R package for clustering cell migration phenotypes from microscopy data

Motivation

The analysis of cell migration using time-lapse microscopy typically focuses on track characteristics for classification and statistical evaluation of migration behaviour. However, considerable heterogeneity can be seen in cell morphology and microscope signal intensity features within the migrating cell populations.

Results

To utilize this information in cell migration analysis, we introduce here an R package CellRomeR, designed for the phenotypic clustering of cells based on their morphological and motility features from microscopy images. Utilizing machine learning techniques and building on an iterative clustering projection method, CellRomeR offers a new approach to identify heterogeneity in cell populations. The clustering of cells along the migration tracks allows association of distinct cellular phenotypes with different cell migration types and detection of migration patterns associated with stable and unstable cell phenotypes. The user-friendly interface of CellRomeR and multiple visualization options facilitate an in-depth understanding of cellular behaviour, addressing previous challenges in clustering cell trajectories using microscope cell tracking data.

Availability and implementation

CellRomeR is available as an R package from https://github.com/elolab/CellRomeR.

PIM kinases regulate early human Th17 cell differentiation

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Single-Cell RNA Sequencing of Human Pluripotent Stem Cell-Derived Macrophages for Quality Control of The Cell Therapy Product

Macrophages exhibit high plasticity to achieve their roles in maintaining tissue homeostasis, innate immunity, tissue repair and regeneration. Therefore, macrophages are being evaluated for cell-based therapeutics against inflammatory disorders and cancer. To overcome the limitation related to expansion of primary macrophages and cell numbers, human pluripotent stem cell (hPSC)-derived macrophages are considered as an alternative source of primary macrophages for clinical application. However, the quality of hPSC-derived macrophages with respect to the biological homogeneity remains still unclear. We previously reported a technique to produce hPSC-derived macrophages referred to as iMACs, which is amenable for scale-up. In this study, we have evaluated the biological homogeneity of the iMACs using a transcriptome dataset of 6,230 iMACs obtained by single-cell RNA sequencing. The dataset provides a valuable genomic profile for understanding the molecular characteristics of hPSC-derived macrophage cells and provide a measurement of transcriptomic homogeneity. Our study highlights the usefulness of single cell RNA-seq data in quality control of the cell-based therapy products.

The long noncoding RNA TUNAR modulates Wnt signaling and regulates human β-cell proliferation

Many long noncoding RNAs (lncRNAs) are enriched in pancreatic islets and several lncRNAs are linked to type 2 diabetes (T2D). Although they have emerged as potential players in β-cell biology and T2D, little is known about their functions and mechanisms in human β-cells. We identified an islet-enriched lncRNA, TUNAR (TCL1 upstream neural differentiation-associated RNA), which was upregulated in β-cells of patients with T2D and promoted human β-cell proliferation via fine-tuning of the Wnt pathway. TUNAR was upregulated following Wnt agonism by a glycogen synthase kinase-3 (GSK3) inhibitor in human β-cells. Reciprocally, TUNAR repressed a Wnt antagonist Dickkopf-related protein 3 (DKK3) and stimulated Wnt pathway signaling. DKK3 was aberrantly expressed in β-cells of patients with T2D and displayed a synchronized regulatory pattern with TUNAR at the single cell level. Mechanistically, DKK3 expression was suppressed by the repressive histone modifier enhancer of zeste homolog 2 (EZH2). TUNAR interacted with EZH2 in β-cells and facilitated EZH2-mediated suppression of DKK3. These findings reveal a novel cell-specific epigenetic mechanism via islet-enriched lncRNA that fine-tunes the Wnt pathway and subsequently human β-cell proliferation.NEW & NOTEWORTHY The discovery that long noncoding RNA TUNAR regulates β-cell proliferation may be important in designing new treatments for diabetes.