Kidney fibrosis is the hallmark of chronic kidney disease progression; however, at present no antifibrotic therapies exist1-3. The origin, functional heterogeneity and regulation of scar-forming cells that occur during human kidney fibrosis remain poorly understood1,2,4. Here, using single-cell RNA sequencing, we profiled the transcriptomes of cells from the proximal and non-proximal tubules of healthy and fibrotic human kidneys to map the entire human kidney. This analysis enabled us to map all matrix-producing cells at high resolution, and to identify distinct subpopulations of pericytes and fibroblasts as the main cellular sources of scar-forming myofibroblasts during human kidney fibrosis. We used genetic fate-tracing, time-course single-cell RNA sequencing and ATAC-seq (assay for transposase-accessible chromatin using sequencing) experiments in mice, and spatial transcriptomics in human kidney fibrosis, to shed light on the cellular origins and differentiation of human kidney myofibroblasts and their precursors at high resolution. Finally, we used this strategy to detect potential therapeutic targets, and identified NKD2 as a myofibroblast-specific target in human kidney fibrosis.

Decoding myofibroblast origins in human kidney fibrosis

Within cells, the spatial compartmentalization of thousands of distinct proteins serves a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) can elucidate protein spatial relationships to global ultrastructure, but has suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional cryogenic SR and focused ion beam-milled block-face EM across entire vitreously frozen cells. The approach preserves ultrastructure while enabling independent SR and EM workflow optimization. We discovered unexpected protein-ultrastructure relationships in mammalian cells including intranuclear vesicles containing endoplasmic reticulum-associated proteins, web-like adhesions between cultured neurons, and chromatin domains subclassified on the basis of transcriptional activity. Our findings illustrate the value of a comprehensive multimodal view of ultrastructural variability across whole cells.

Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells

Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics. Availability All the source code and data are available at https://github.com/kuanglab/single-cell-review.

Machine learning and statistical methods for clustering single-cell RNA-sequencing data.

Emerging role of PI3K/AKT in tumor-related epigenetic regulation

Living cells function through the spatial compartmentalization of thousands of distinct proteins serving a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) has emerged as a pathway to directly view nanoscale protein relationships to the underlying global ultrastructure, but has traditionally suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional correlative cryogenic SR and focused ion beam milled block-face EM across entire vitreously frozen cells that addresses these issues by preserving native ultrastructure and enabling independent SR and EM workflow optimization. Application to a variety of biological systems revealed a number of unexpected protein-ultrastructure relationships and underscored the value of a comprehensive multimodal view of ultrastructural variability across whole cells.

https://www.biorxiv.org/content/biorxiv/early/2019/09/18/773986.full.pdf

Correlative three-dimensional super-resolution and block face electron microscopy of whole vitreously frozen cells

Balanced symmetric and asymmetric divisions of neural progenitor cells (NPCs) are crucial for brain development, but the underlying mechanisms are not fully understood. Here we report that mitotic kinesin KIF20A/MKLP2 interacts with RGS3 and plays a crucial role in controlling the division modes of NPCs during cortical neurogenesis. Knockdown of KIF20A in NPCs causes dislocation of RGS3 from the intercellular bridge (ICB), impairs the function of Ephrin-B–RGS cell fate signaling complex, and leads to a transition from proliferative to differentiative divisions. Germline and inducible knockout of KIF20A causes a loss of progenitor cells and neurons and results in thinner cortex and ventriculomegaly. Interestingly, loss of function of KIF20A induces early cell cycle exit and precocious neuronal differentiation without causing substantial cytokinesis defect or apoptosis. Our results identify a RGS–KIF20A axis in the regulation of cell division and suggest a potential link of the ICB to regulation of cell fate determination. The division of neural progenitors is closely regulated but how is unclear. Here, the authors show that mitotic kinesin KIF20A/MKLP2 interacts with a regulator of G protein signaling RGS3 in neural progenitor cells, dislodging it from the intercellular bridge of dividing cortical cells.

/pdf/kif20a-mklp2-regulates-the-division-modes-of-neural-14fbuw86in.pdf

KIF20A/MKLP2 regulates the division modes of neural progenitor cells during cortical development.

Dynamics of RNA Polymerase II Pausing and Bivalent Histone H3 Methylation during Neuronal Differentiation in Brain Development.

Research interests toward single cell analysis have greatly increased in basic, translational and clinical research areas recently, as advances in whole-transcriptome amplification technique allow scientists to get accurate sequencing result at single cell level. An important step in the single-cell transcriptome analysis is to identify distinct cell groups that have different gene expression patterns. Currently there are limited bioinformatics approaches available for single-cell RNA-seq analysis. Many studies rely on principal component analysis (PCA) with arbitrary parameters to identify the genes that will be used to cluster the single cells. We have developed a novel algorithm, called SAIC (Single cell Analysis via Iterative Clustering), that identifies the optimal set of signature genes to separate single cells into distinct groups. Our method utilizes an iterative clustering approach to perform an exhaustive search for the best parameters within the search space, which is defined by a number of initial centers and P values. The end point is identification of a signature gene set that gives the best separation of the cell clusters. Using a simulated data set, we showed that SAIC can successfully identify the pre-defined signature gene sets that can correctly separated the cells into predefined clusters. We applied SAIC to two published single cell RNA-seq datasets. For both datasets, SAIC was able to identify a subset of signature genes that can cluster the single cells into groups that are consistent with the published results. The signature genes identified by SAIC resulted in better clusters of cells based on DB index score, and many genes also showed tissue specific expression. In summary, we have developed an efficient algorithm to identify the optimal subset of genes that separate single cells into distinct clusters based on their expression patterns. We have shown that it performs better than PCA method using published single cell RNA-seq datasets.

/pdf/saic-an-iterative-clustering-approach-for-analysis-of-single-3ff4sp6h55.pdf

SAIC: an iterative clustering approach for analysis of single cell RNA-seq data.

Alternative pre-mRNA splicing (AS) produces multiple isoforms of mRNAs and proteins from a single gene. It is most prevalent in the mammalian brain and is thought to contribute to the formation and/or maintenance of functional complexity of the brain. Increasing evidence has documented the significant changes of AS between different regions or different developmental stages of the brain, however, the dynamics of AS and the possible function of it during neural progenitor cell (NPC) differentiation is less well known. Here, using purified NPCs and their progeny neurons isolated from the embryonic mouse cerebral cortex, we characterized the global differences of AS events between the 2 cell types by deep sequencing. The sequencing results revealed cell type-specific AS in NPCs and neurons that are important for distinct functions pertinent to the corresponding cell type. Our data may serve as a resource useful for further understanding how AS contributes to molecular regulations in NPCs and neurons during cortical development.

/pdf/alternative-rna-splicing-associated-with-mammalian-neuronal-1ys7g9jxuv.pdf

Alternative RNA Splicing Associated With Mammalian Neuronal Differentiation

In the cerebral cortex, projection neurons and interneurons work coordinately to establish neural networks for normal cortical functions. While the specific mechanisms that control productions of projection neurons and interneurons are beginning to be revealed, a global characterization of the molecular differences between these two neuron types is crucial for a more comprehensive understanding of their developmental specifications and functions. In this study, using lineage tracing power of combining Tbr2(Eomes)-GFP and Dcx-mRFP reporter mice, we prospectively separated intermediate progenitor cell (IPC)-derived neurons (IPNs) from non-IPC-derived neurons (non-IPNs) of the embryonic cerebral cortex. Molecular characterizations revealed that IPNs and non-IPNs were enriched with projection neurons and interneurons, respectively. Expression profiling documented cell-specific genes including differentially expressed transcriptional regulators that might be involved in cellular specifications, for instance, our data found that SOX1 and SOX2, which were known for important functions in neural stem/progenitor cells, continued to be expressed by interneurons but not by projection neurons. Transcriptome analyses of cortical neurons isolated at different stages of neurogenesis revealed distinct temporal patterns of expression of genes involved in early-born or late-born neuron specification. These data present a resource useful for further investigation of the molecular regulations and functions of projection neurons and interneurons.

/pdf/prospective-separation-and-transcriptome-analyses-of-15qyh7d8sg.pdf

Prospective separation and transcriptome analyses of cortical projection neurons and interneurons based on lineage tracing by Tbr2 (Eomes)‐GFP/Dcx‐mRFP reporters

Jiancheng Liu

Papers

KIF20A/MKLP2 regulates the division modes of neural progenitor cells during cortical development.

Dynamics of RNA Polymerase II Pausing and Bivalent Histone H3 Methylation during Neuronal Differentiation in Brain Development.

SAIC: an iterative clustering approach for analysis of single cell RNA-seq data.

Alternative RNA Splicing Associated With Mammalian Neuronal Differentiation

Prospective separation and transcriptome analyses of cortical projection neurons and interneurons based on lineage tracing by Tbr2 (Eomes)‐GFP/Dcx‐mRFP reporters