A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys

Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT(+)) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types

Identification of a specific reprogramming-associated epigenetic signature in human induced pluripotent stem cells

Generation of human induced pluripotent stem cells (hiPSCs) by the expression of specific transcription factors depends on successful epigenetic reprogramming to a pluripotent state. Although hiPSCs and human embryonic stem cells (hESCs) display a similar epigenome, recent reports demonstrated the persistence of specific epigenetic marks from the somatic cell type of origin and aberrant methylation patterns in hiPSCs. However, it remains unknown whether the use of different somatic cell sources, encompassing variable levels of se- lection pressure during reprogramming, influences the level of epigenetic aberrations in hiPSCs. In this work, we characterized the epigenomic integrity of 17 hiPSC lines derived from six different cell types with varied reprogramming efficiencies. We demonstrate that epigenetic aberrations are a general feature of the hiPSC state and are independent of the somatic cell source. Interestingly, we observe that the reprogramming efficiency of somatic cell lines inversely correlates with the amount of methylation change needed to acquire pluripotency. Additionally, we determine that both shared and line- specific epigenetic aberrations in hiPSCs can directly translate into changes in gene expression in both the pluripotent and differenti- ated states. Significantly, our analysis of different hiPSC lines from multiple cell types of origin allow us to identify a reprogramming- specific epigenetic signature comprised of nine aberrantly methyl- ated genes that is able to segregate hESC and hiPSC lines regardless of the somatic cell source or differentiation state

Comparative cellular analysis of motor cortex in human, marmoset and mouse

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Probing Interaction of Genome and Methylome by Targeted Bisulfite Sequencing

DNA methylation at CpG dinucleotides in mammalian cells is recognized as an epigenetic mechanism that plays a major role in mammalian development via gene expression regulation. Techniques in DNA methylation profiling have been advancing in the past decades. I have developed the second-generation of bisulfite padlock probe (BSPP) method, which does not require multiple steps of standard library preparation. This method is high-throughput and more scalable for quantification of DNA methylation at single-base resolution. The library-free method greatly reduces sample-preparation time and cost and is also compatible with automation. These developments have fulfilled the key requirements of a DNA methylation assay, including cost effectiveness, minimum sample input requirements, accuracy, and throughput. I have performed this technique to compare with other assays performed by different research groups for locus-specific DNA methylation analysis on the same samples set. BSPP assay showed a high correlation with other assays that have highest accuracy and is at the top with other assays based on the throughput. Genetic variants have an impact on local DNA methylation patterns by influencing methyltransferase recognition sequences or altering the DNA binding affinity of cis-regulatory proteins. To study this interaction, I have characterized CpG methylation state of 96 individuals from 22 nuclear pedigrees consisting of 52 parent-child trios using BSPP. I used the DMR330k probe set to quantify DNA methylation level at a set of 411,800 CpGs. Next, I have employed three independent approaches, including mid-parent offspring (MPO), methylation quantitative trait loci (mQTL), and allele-specific methylation (ASM) analysis, to investigate the influence of genetic polymorphisms on DNA methylation variation. MPO analysis identified 10,593 heritable CpG sites, among which 70.1% were SNPs that present in CpG sites. With mQTL analysis, 49.9% of heritable CpG sites were identified where regulation occurred in a distal cis-regulatory manner while ASM analysis was only able to identify 5% of heritable CpGs. This finding suggested that mQTL analysis do not identify all the cis-regulartory SNPs associated with heritable CpG methylation, and ASM analysis has even less power. I have extensively proved that in addition to regulating the mean of DNA methylation, genetic polymorphisms are also associated with the variability of DNA methylation levels. I have identified hundreds of CpG clusters in human genome for which the degree of DNA methylation variability was associated with genetic polymorphisms. This finding supported the previous studies showing that genetic variants have the influence on phenotypic plasticity such as gene expression or DNA methylation

Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2.

The COVID-19 pandemic has created an urgent need for effective therapeutic and diagnostic strategies to manage the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the emergence of numerous variants of concern (VOCs) has made it challenging to develop targeted therapies that are broadly specific in neutralizing the virus. In this study, we aimed to develop neutralizing nanobodies (Nbs) using computational techniques that can effectively neutralize the receptor-binding domain (RBD) of SARS-CoV-2 VOCs. We evaluated the performance of different protein-protein docking programs and identified HDOCK as the most suitable program for Nb/RBD docking with high accuracy. Using this approach, we designed 14 novel Nbs with high binding affinity to the VOC RBDs. The Nbs were engineered with mutated amino acids that interacted with key amino acids of the RBDs, resulting in higher binding affinity than human angiotensin-converting enzyme 2 (ACE2) and other viral RBDs or haemagglutinins (HAs). The successful development of these Nbs demonstrates the potential of molecular modeling as a low-cost and time-efficient method for engineering effective Nbs against SARS-CoV-2. The engineered Nbs have the potential to be employed in RBD-neutralizing assays, facilitating the identification of novel treatment, prevention, and diagnostic strategies against SARS-CoV-2

The top 10 VMR clusters and their associated genes.

<p>The genes in bold text expressed at detectible level in whole blood and were selected for association testing.</p