Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics

Split Luciferase-Fragment Reconstitution for Unveiling RNA Localization and Dynamics in Live Cells

The intracellular distribution and dynamics of RNAs play pivotal roles in various physiological phenomena. The ability to monitor the amount and localization of endogenous RNAs in living cells allows for elucidating the mechanisms of various intracellular events. Protein-based fluorescent RNA probes are now widely used to visualize and analyze RNAs in living cells. However, continuously monitoring the temporal changes in RNA localization and dynamics in living cells is challenging. In this study, we developed a bioluminescent probe for spatiotemporal monitoring of RNAs in living cells by using a split-luciferase reconstitution technique. The probe consists of split fragments of a bioluminescent protein, NanoLuc, connected with RNA-binding protein domains generated from a custom-made mutation of a PUM-HD. The probe showed rapid luminescence intensity changes in response to an increase or decrease in the amount of a target RNA in vitro. In live-cell imaging, temporal alteration of the intracellular distribution of endogenous β-actin mRNA was visualized in response to extracellular stimulation. Furthermore, the application of the probe to the visualization of the specific localization of β-actin mRNA in primary hippocampal neurons was conducted. These results demonstrate the capability of the bioluminescent RNA probe to monitor the changes in localization, dynamics, and the amount of target RNA in living cells.

Micro-Raman spectroscopy of the light-harvesting pigments in Chlamydomonas reinhardtii under salinity stress

Microalgae are a rich source of carotenoids with enhanced yields during biotic or abiotic stresses, which often impose survival challenges on the cells. Using a non-invasive pigment profiling approach with micro-Raman spectroscopy, we have analyzed the effect of salinity stress on carotenoids in autotrophic Chlamydomonas reinhardtii. Raman spectral analysis of ν(C = C) mode indicates an increase in the carotenoids with lower conjugation length (lutein and zeaxanthin) compared to β-carotene, as the function of culture age and salinity stress, but especially when salinity stress was imposed in two-stage mode (stress imposed on 2nd day, D2_100, and 4th day, D4_100, during exponential phase). Population-scale heterogeneities in carotenoid Raman mode peak center, quantified with heterogeneity index (HI), were highest during the stationary phase of the cultures and under salinity stress. Although the Raman signal was obtained from a randomly selected small focal volume in the cell, a decrease in chlorophyll Raman mode intensities with age and salinity stress was well corroborated by single-cell population fraction measurements by microscopy. Raman intensity fluctuations (I_f) were high for both chlorophyll and carotenoid modes under salinity stress, which can arise due to variations in chlorophyll/carotenoid content and composition, or conformational changes in the pigments in C. reinhardtii cells. Interestingly, in all growth conditions, chlorophyll a Raman mode intensity was found to show a high correlation to that of β-carotene, pointing out a high degree of cooperativity in the light-harvesting complex pigments even during salinity stress. Thus, we demonstrate the usefulness of non-invasive pigment profiling with micro-Raman spectroscopy for developing an optimization for salinity stress conditions for high biomass yield and proper harvest time to obtain carotenoids with desired chemical composition.

EMBEDR: Distinguishing signal from noise in single-cell omics data

Single-cell “omics”-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real—not spurious—structure. EMBEDR generates a “p” value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample’s neighborhood can thus be determined and used to generate a novel “cell-wise optimal” embedding. EMBEDR is available as a Python package for immediate use.

An overview of the benefits and difficulties of dimensionality reduction

A novel algorithm for quantifying and identifying quality within embeddings of data

Quality can be optimized to find data scales and set algorithm parameters

A cell-wise view of quality generates robust and interpretable representations of data

Modern technologies have enabled biologists to construct enormous datasets containing millions of observations of thousands of measurements. These datasets push the limits of traditional analysis techniques, leaving doubts about the quality and fidelity of these methods. In this work, we present a sort of meta-algorithm, called EMBEDR, which seeks to evaluate when a certain class of methods, known as dimensionality reduction methods, are generating high-quality representations of data. We show that EMBEDR allows for visualizations of even large datasets to be interpreted with confidence. Furthermore, we show how asking about the method quality itself can lead to improved analyses of data. These improved analyses may directly impact our understanding of cellular biology, including how cells behave, grow, and respond to stimuli.

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.