Unbiased integration of single cell transcriptome replicates

Regnase-1 D141N mutation induces CD4+ T cell-mediated lung granuloma formation via upregulation of Pim2

Regnase-1 is an RNase that plays a critical role in negatively regulating immune responses by destabilizing inflammatory messenger RNAs (mRNAs). Dysfunction of Regnase-1 can be a major cause of various inflammatory diseases with tissue injury and immune cell infiltration into organs. This study focuses on the role of the RNase activity of Regnase-1 in developing inflammatory diseases. We have constructed mice with a single point mutation at the catalytic center of the Regnase-1 RNase domain, which lacks endonuclease activity. D141N mutant mice demonstrated systemic inflammation, immune cell infiltration into various organs, and progressive development of lung granuloma. CD4+ T cells, mainly affected by this mutation, upregulated the mTORC1 pathway and facilitated the autoimmune trait in the D141N mutation. Moreover, serine/threonine kinase Pim2 contributed to lung inflammation in this mutation. Inhibition of Pim2 kinase activity ameliorated granulomatous inflammation, immune cell infiltration, and proliferation in the lungs. Additionally, Pim2 inhibition reduced the expression of adhesion molecules on CD4+ T cells, suggesting a role for Pim2 in facilitating leukocyte adhesion and migration to inflamed tissues. Our findings provide new insights into the role of Regnase-1 RNase activity in controlling immune functions and underscore the therapeutic relevance of targeting Pim2 to modulate abnormal immune responses.

Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19

Here, we performed single-cell RNA sequencing of S1 and receptor binding domain protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells (MBCs) in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as GADD45B, MAP3K8, and NFKBIA in critical and severe individuals. Furthermore, the analysis of atypical MBCs suggested a developmental pathway similar to that of conventional MBCs through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including CXCR4, CXCR5, BCL2, and MYC. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as ZFP36 and DUSP1, suggested that the differentiation and activation of atypical MBCs may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical MBCs in COVID-19 and the role of other B cell subsets across different clinical manifestations.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

3D model of mouse embryonic pancreas and endocrine compartment using stem cell-derived mesoderm and pancreatic progenitors

The developing mouse pancreas is surrounded by mesoderm compartments providing signals that induce pancreas formation. Most pancreatic organoid protocols lack this mesoderm niche and only partially capture the pancreatic cell repertoire. This work aims to generate pancreatic aggregates by differentiating mouse embryonic stem cells (mESCs) into mesoderm progenitors (MPs) and pancreas progenitors (PPs), without using Matrigel. First, mESCs were differentiated into epiblast stem cells (EpiSCs) to enhance the PP differentiation rate. Next, PPs and MPs aggregated together giving rise to various pancreatic cell types, including endocrine, acinar, and ductal cells, and to endothelial cells. Single-cell RNA sequencing analysis revealed a larger endocrine population within the PP + MP aggregates, as compared to PPs alone or PPs in Matrigel aggregates. The PP + MP aggregate gene expression signatures and its endocrine population percentage closely resembled those of the endocrine population found in the mouse embryonic pancreas, which holds promise for studying pancreas development.

Multi-omics computational analysis unveils the involvement of AP-1 and CTCF in hysteresis of chromatin states during macrophage polarization

Macrophages display extreme plasticity, and the mechanisms and applications of polarization and de-/repolarization of macrophages have been extensively investigated. However, the regulation of macrophage hysteresis after de-/repolarization remains unclear. In this study, by using a large-scale computational analysis of macrophage multi-omics data, we report a list of hysteresis genes that maintain their expression patterns after polarization and de-/repolarization. While the polarization in M1 macrophages leads to a higher level of hysteresis in genes associated with cell cycle progression, cell migration, and enhancement of the immune response, we found weak levels of hysteresis after M2 polarization. During the polarization process from M0 to M1 and back to M0, the factors IRFs/STAT, AP-1, and CTCF regulate hysteresis by altering their binding sites to the chromatin. Overall, our results show that a history of polarization can lead to hysteresis in gene expression and chromatin accessibility over a given period. This study contributes to the understanding of de-/repolarization memory in macrophages.

Omics data integration in computational biology viewed through the prism of machine learning paradigms

Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years.

Construction of a T cell receptor signaling range for spontaneous development of autoimmune disease

Thymic selection and peripheral activation of conventional T (Tconv) and regulatory T (Treg) cells depend on TCR signaling, whose anomalies are causative of autoimmunity. Here, we expressed in normal mice mutated ZAP-70 molecules with different affinities for the CD3 chains, or wild type ZAP-70 at graded expression levels under tetracycline-inducible control. Both manipulations reduced TCR signaling intensity to various extents and thereby rendered those normally deleted self-reactive thymocytes to become positively selected and form a highly autoimmune TCR repertoire. The signal reduction more profoundly affected Treg development and function because their TCR signaling was further attenuated by Foxp3 that physiologically repressed the expression of TCR-proximal signaling molecules, including ZAP-70, upon TCR stimulation. Consequently, the TCR signaling intensity reduced to a critical range generated pathogenic autoimmune Tconv cells and concurrently impaired Treg development/function, leading to spontaneous occurrence of autoimmune/inflammatory diseases, such as autoimmune arthritis and inflammatory bowel disease. These results provide a general model of how altered TCR signaling evokes autoimmune disease.

Leveraging data-driven self-consistency for high-fidelity gene expression recovery

Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the expression levels are similarly distributed in different parts of the matrix. We show that SERM improves the accuracy of gene imputation with orders of magnitude enhancement in computational efficiency in comparison to the state-of-the-art imputation techniques.