Transcriptional characterization of iPSC-derived microglia as a model for therapeutic development in neurodegeneration.

Microglia are the resident immune cells in the brain that play a key role in driving neuroinflammation, a hallmark of neurodegenerative disorders. Inducible microglia-like cells have been developed as an in vitro platform for molecular and therapeutic hypothesis generation and testing. However, there has been no systematic assessment of similarity of these cells to primary human microglia along with their responsiveness to external cues expected of primary cells in the brain. In this study, we performed transcriptional characterization of commercially available human inducible pluripotent stem cell (iPSC)-derived microglia-like (iMGL) cells by bulk and single cell RNA sequencing to assess their similarity with primary human microglia. To evaluate their stimulation responsiveness, iMGL cells were treated with Liver X Receptor (LXR) pathway agonists and their transcriptional responses characterized by bulk and single cell RNA sequencing. Bulk transcriptome analyses demonstrate that iMGL cells have a similar overall expression profile to freshly isolated human primary microglia and express many key microglial transcription factors and functional and disease-associated genes. Notably, at the single-cell level, iMGL cells exhibit distinct transcriptional subpopulations, representing both homeostatic and activated states present in normal and diseased primary microglia. Treatment of iMGL cells with LXR pathway agonists induces robust transcriptional changes in lipid metabolism and cell cycle at the bulk level. At the single cell level, we observe heterogeneity in responses between cell subpopulations in homeostatic and activated states and deconvolute bulk expression changes into their corresponding single cell states. In summary, our results demonstrate that iMGL cells exhibit a complex transcriptional profile and responsiveness, reminiscent of in vivo microglia, and thus represent a promising model system for therapeutic development in neurodegeneration.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

BACKGROUND: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS: We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

Organotypic Brain Slice Culture Microglia Exhibit Molecular Similarity to Acutely-Isolated Adult Microglia and Provide a Platform to Study Neuroinflammation.

Microglia are central nervous system (CNS) resident immune cells that have been implicated in neuroinflammatory pathogenesis of a variety of neurological conditions. Their manifold context-dependent contributions to neuroinflammation are only beginning to be elucidated, which can be attributed in part to the challenges of studying microglia in vivo and the lack of tractable in vitro systems to study microglia function. Organotypic brain slice cultures offer a tissue-relevant context that enables the study of CNS resident cells and the analysis of brain slice microglial phenotypes has provided important insights, in particular into neuroprotective functions. Here we use RNA sequencing, direct digital quantification of gene expression with nCounter® technology and targeted analysis of individual microglial signature genes, to characterize brain slice microglia relative to acutely-isolated counterparts and 2-dimensional (2D) primary microglia cultures, a widely used in vitro surrogate. Analysis using single cell and population-based methods found brain slice microglia exhibited better preservation of canonical microglia markers and overall gene expression with stronger fidelity to acutely-isolated adult microglia, relative to in vitro cells. We characterized the dynamic phenotypic changes of brain slice microglia over time, after plating in culture. Mechanical damage associated with slice preparation prompted an initial period of inflammation, which resolved over time. Based on flow cytometry and gene expression profiling we identified the 2-week timepoint as optimal for investigation of microglia responses to exogenously-applied stimuli as exemplified by treatment-induced neuroinflammatory changes observed in microglia following LPS, TNF and GM-CSF addition to the culture medium. Altogether these findings indicate that brain slice cultures provide an experimental system superior to in vitro culture of microglia as a surrogate to investigate microglia functions, and the impact of soluble factors and cellular context on their physiology.

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time.

Single-cell RNA sequencing (scRNA-seq) is an exciting new technology allowing the analysis of transcriptomes from individual cells, and is ideally suited to address the inherent complexity and dynamics of the central nervous system. scRNA-seq has already been applied to the study of molecular taxonomy of the brain. These works have paved the way to expanding our understanding of the nervous system and provide insights into cellular susceptibilities and molecular mechanisms in neurological and neurodegenerative diseases. We discuss recent progress and challenges in applying this technology to advance our understanding of the brain. We advocate the application of scRNA-seq in the discovery of targets and biomarkers as a new approach in developing novel therapeutics for the treatment of neurodegenerative diseases.

Scarless Gene Tagging with One-Step Transformation and Two-Step Selection in Saccharomyces cerevisiae and Schizosaccharomyces pombe.

Gene tagging with fluorescent proteins is commonly applied to investigate the localization and dynamics of proteins in their cellular environment. Ideally, a fluorescent tag is genetically inserted at the endogenous locus at the N- or C- terminus of the gene of interest without disrupting regulatory sequences including the 5' and 3' untranslated region (UTR) and without introducing any extraneous unwanted "scar" sequences, which may create unpredictable transcriptional or translational effects. We present a reliable, low-cost, and highly efficient method for the construction of such scarless C-terminal and N-terminal fusions with fluorescent proteins in yeast. The method relies on sequential positive and negative selection and uses an integration cassette with long flanking regions, which is assembled by two-step PCR, to increase the homologous recombination frequency. The method also enables scarless tagging of essential genes with no need for a complementing plasmid. To further ease high-throughput strain construction, we have computationally automated design of the primers, applied the primer design code to all open reading frames (ORFs) of the budding yeast Saccharomyces cerevisiae (S. cerevisiae) and the fission yeast Schizosaccharomyces pombe (S. pombe), and provide here the computed sequences. To illustrate the scarless N- and C-terminal gene tagging methods in S. cerevisiae, we tagged various genes including the E3 ubiquitin ligase RSP5, the proteasome subunit PRE1, and the eleven Rab GTPases with yeast codon-optimized mNeonGreen or mCherry; several of these represent essential genes. We also implemented the scarless C-terminal gene tagging method in the distantly related organism S. pombe using kanMX6 and HSV1tk as positive and negative selection markers, respectively, as well as ura4. The scarless gene tagging methods presented here are widely applicable to visualize and investigate the functional roles of proteins in living cells.

Mechanical slowing-down of cytoplasmic diffusion allows in vivo counting of proteins in individual cells.

Many key regulatory proteins in bacteria are present in too low numbers to be detected with conventional methods, which poses a particular challenge for single-cell analyses because such proteins can contribute greatly to phenotypic heterogeneity. Here we develop a microfluidics-based platform that enables single-molecule counting of low-abundance proteins by mechanically slowing-down their diffusion within the cytoplasm of live Escherichia coli (E. coli) cells. Our technique also allows for automated microscopy at high throughput with minimal perturbation to native physiology, as well as viable enrichment/retrieval. We illustrate the method by analysing the control of the master regulator of the E. coli stress response, RpoS, by its adapter protein, SprE (RssB). Quantification of SprE numbers shows that though SprE is necessary for RpoS degradation, it is expressed at levels as low as 3-4 molecules per average cell cycle, and fluctuations in SprE are approximately Poisson distributed during exponential phase with no sign of bursting.

Accurate concentration control of mitochondria and nucleoids.

All cellular materials are partitioned between daughters at cell division, but by various mechanisms and with different accuracy. In the yeast Schizosaccharomyces pombe, the mitochondria are pushed to the cell poles by the spindle. We found that mitochondria spatially reequilibrate just before division, and that the mitochondrial volume and DNA-containing nucleoids instead segregate in proportion to the cytoplasm inherited by each daughter. However, nucleoid partitioning errors are suppressed by control at two levels: Mitochondrial volume is actively distributed throughout a cell, and nucleoids are spaced out in semiregular arrays within mitochondria. During the cell cycle, both mitochondria and nucleoids appear to be produced without feedback, creating a net control of fluctuations that is just accurate enough to avoid substantial growth defects.

Random partitioning of molecules at cell division.

Many RNAs, proteins, and organelles are present in such low numbers per cell that random segregation of individual copies causes large "partitioning errors" at cell division. Even symmetrically dividing cells can then by chance produce daughters with very different composition. The size of the errors depends on the segregation mechanism: Control systems can reduce low-abundance errors, but the segregation process can also be subject to upstream sources of randomness or spatial heterogeneities that create large errors despite high abundances. Here we mathematically demonstrate how partitioning errors arise for different types of segregation mechanisms and how errors can be greatly increased by upstream heterogeneity but remarkably hard to avoid through controlled partitioning. We also show that seemingly straightforward experiments cannot be straightforwardly interpreted because very different mechanisms produce identical fits and present an approach to deal with this problem by adding binomial counting noise and testing for convexity or concavity in the partitioning error as a function of the binomial thinning parameter. The results lay a conceptual groundwork for more effective studies of heterogeneity among growing and dividing cells, whether in microbes or in differentiating tissues.

Non-genetic heterogeneity from stochastic partitioning at cell division.

Gene expression involves inherently probabilistic steps that create fluctuations in protein abundances. The results from many in-depth analyses and genome-scale surveys have suggested how such fluctuations arise and spread, often in ways consistent with stochastic models of transcription and translation. But fluctuations also arise during cell division when molecules are partitioned stochastically between the two daughters. Here we mathematically demonstrate how stochastic partitioning contributes to the non-genetic heterogeneity. Our results show that partitioning errors are hard to correct, and that the resulting noise profiles are remarkably difficult to separate from gene expression noise. By applying these results to common experimental strategies and distinguishing between creation versus transmission of noise, we hypothesize that much of the cell-to-cell heterogeneity that has been attributed to various aspects of gene expression instead comes from random segregation at cell division. We propose experiments to separate between these two types of fluctuations and discuss future directions.