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Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain

Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. In this study, we analyzed the RNA expression patterns of more than 76,000 individual microglia during development, old age and after brain injury. Analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states - including chemokine-enriched inflammatory microglia - persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human MS lesions. These unique microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

neuroscience↗

bioRxiv2019

Single-Cell RNA Sequencing Reveals Regulatory Mechanism for Trophoblast Cell-Fate Divergence in Human Peri-Implantation Embryo

Multipotent trophoblasts undergo dynamic morphological movement and cellular differentiation after embryonic implantation to generate placenta. However, the mechanism controlling trophoblast development and differentiation during peri-implantation development remains elusive. In this study, we modeled human embryo peri-implantation development from blastocyst to early post-implantation stages by using an in vitro coculture system, and profiled the transcriptome of individual trophoblast cells from these embryos. We revealed the genetic networks regulating peri-implantation trophoblast development. While determining when trophoblast differentiation happens, our bioinformatic analysis identified T-box transcription factor 3 (TBX3) as a key regulator for the differentiation of cytotrophoblast into syncytiotrophoblast. The function of TBX3 in trophoblast differentiation is then validated by a loss-of-function experiment. In conclusion, our results provided a valuable resource to study the regulation of trophoblasts development and differentiation during human peri-implantation development.

cell biology↗

bioRxiv2021

Tree-based Correlation Screen and Visualization for Exploring Phenotype-Cell Type Association in Multiple Sample Single-Cell RNA-Sequencing Experiments

Single-cell RNA-seq experiments with multiple samples are increasingly used to discover cell types and their molecular features that may influence samples phenotype (e.g. disease). However, analyzing and visualizing the complex cell type-phenotype association remains nontrivial. TreeCorTreat is an open source R package that tackles this problem by using a tree-based correlation screen to analyze and visualize the association between phenotype and transcriptomic features and cell types at multiple cell type resolution levels. With TreeCorTreat, one can conveniently explore and compare different feature types, phenotypic traits, analysis protocols and datasets, and evaluate the impacts of potential confounders.

bioinformatics↗

bioRxiv2016

Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state

The T cell compartment must contain diversity in both TCR repertoire and cell state to provide effective immunity against pathogens1,2. However, it remains unclear how differences in the TCR contribute to heterogeneity in T cell state at the single cell level because most analysis of the TCR repertoire has, to date, aggregated information from populations of cells. Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR sequence and global transcriptional profile from single cells. However, current protocols to directly sequence the TCR require the use of long sequencing reads, increasing the cost and decreasing the number of cells that can be feasibly analyzed. Here we present a tool that can efficiently extract TCR sequence information from standard, short-read scRNA-seq libraries of T cells: TCR Reconstruction Algorithm for Paired-End Single cell (TRAPeS). We apply it to investigate heterogeneity in the CD8+ T cell response in humans and mice, and show that it is accurate and more sensitive than previous approaches3,4. We applied TRAPeS to single cell RNA-seq of CD8+ T cells specific for a single epitope from Yellow Fever Virus5. We show that the recently-described \"naive-like\" memory population of YFV-specific CD8+ T cells have significantly longer CDR3 regions and greater divergence from germline sequence than do effector-memory phenotype CD8+ T cells specific for YFV. This suggests that TCR usage contributes to heterogeneity in the differentiation state of the CD8+ T cell response to YFV. TRAPeS is publicly available, and can be readily used to investigate the relationship between the TCR repertoire and cellular phenotype.

Bioinformatics↗

bioRxiv2018

Reconstructing B cell receptor sequences from short-read single cell RNA-sequencing with BRAPeS

RNA-sequencing of single B cells provides simultaneous measurements of the cell state and its binding specificity. However, in order to uncover the latter further reconstruction of the B cell receptor (BCR) sequence is needed. We present BRAPeS, an algorithm for reconstructing BCRs from short-read paired-end single cell RNA-sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared to long reads. BRAPeS is publicly available in the following link: https://github.com/YosefLab/BRAPeS.

bioinformatics↗

bioRxiv2023

Single-cell Mayo Map (scMayoMap): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis

Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. scMayoMap performs better than the currently available annotation tools on all the datasets tested. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

bioinformatics↗

bioRxiv2022

IBRAP: Integrated Benchmarking Single-cell RNA-sequencing Analytical Pipeline

Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to study cellular heterogeneity. The high dimensional data generated from this technology are complex and require specialised expertise for analysis and interpretation. The core of scRNA-seq data analysis contains several key analytical steps, which include pre-processing, QC, normalisation, dimensionality reduction, integration, and clustering. Each step often has many algorithms developed with varied underlying assumptions and implications. With such a diverse choice of tools available, benchmarking analyses have compared their performances and demonstrated that tools differentially operate according to the data types and complexity. Here, we present Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP) - a tool, which contains a range of analytical components that can be interchanged throughout the pipeline alongside multiple benchmarking metrics that enables users to compare results and determine the optimal pipeline combinations for their data. We apply IBRAP to single and multi-sample integration analysis using pancreas, cell line and simulated data accompanied with ground truth cell labels, demonstrating the interchangeable and benchmarking functionality of IBRAP. Our results confirm that the optimal pipelines are dependant of individual samples and studies, further supporting the rationale and necessity of our tool. We then compare reference-based cell annotation with unsupervised analysis, both included in IBRAP, and demonstrate the superiority of the reference-based method in identifying robust major and minor cell types. Thus, IBRAP presents a valuable tool to integrate multiple samples and studies to create reference maps of normal and diseased tissues, facilitating novel biological discovery using the vast volume of scRNA-seq data available.

bioinformatics↗

bioRxiv2026

Analysis of Transcriptograms in Epithelial-Mesenchymal Transition (EMT)

Single-cell RNA sequencing (single-cell RNA-seq) has represented a revolution in gene expression analysis. However, high dropout rates and stochastic noise often reduce the amount of information captured in these experiments. The epithelial-mesenchymal transition (EMT), which is fundamental to tumor progression and organismal development, is particularly difficult to fully characterize due to the existence of intermediate states. In this work, we demonstrate that projecting transcriptomic data onto gene lists ordered using protein-protein interaction (PPI) information acts as a "biological low-pass filter", attenuating technical noise and increasing the statistical power of the analyses. We propose and validate an innovative pipeline that integrates the Transcriptogram method with Principal Component Analysis (PCA). By applying a moving average over functionally ordered genes, we drastically increase the signal-to-noise ratio, enabling the inference of cellular trajectories. The method was applied to a public dataset of TGF-{beta}1-induced MCF10A cells, with rigorous batch-effect correction based on biological controls. The results reveal that EMT is not merely a morphological change, but a coordinated, systemic reprogramming. This approach enabled the identification of critical modules that would remain hidden in conventional analyses: (i) a massive "Metabolic Switch" (Cluster 2), indicating a transition toward oxidative phosphorylation to sustain invasion; (ii) a strategic blockade of the cell cycle (Cluster 4); and (iii) a "Detoxification Shield" and chemoresistance program (Cluster 5), characterized by endogenous activation of metallothioneins. We conclude that the combination of PPI network topology and dimensionality reduction offers superior resolution for dissecting cellular plasticity. The method not only validates classical markers, but also reveals the hidden functional architecture of the transition, showing that EMT is not a single, uniform process, but rather one in which cells can follow distinct trajectories, halting at different stages of differentiation.

bioinformatics↗

bioRxiv2019

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing

The libraries generated by high-throughput single cell RNA-sequencing platforms such as the Chromium from 10X Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use this data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3 libraries consisting of over 70,000 cells. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and cell, UMI, and gene detection. However, compared with the NextSeq 500, the MGISEQ-2000 platform performs consistently better, identifying more cells, genes, and UMIs at equalised read depth. We were able to call an additional 1,065,659 SNPs from sequence data generated by the BGI platform, enabling an additional 14% of cells to be assigned to the correct donor from a multiplexed library. However, both the NextSeq 500 and MGISEQ-2000 detected similar frequencies of gRNAs from a pooled CRISPR single cell screen. Our study provides a benchmark for high capacity sequencing platforms applied to high-throughput single cell RNA-seq libraries.

genomics↗

bioRxiv2025

Highly sensitive and scalable time-resolved RNA sequencing in single cells with scNT-seq2

Understanding gene expression dynamics requires resolving newly synthesized RNAs from pre-existing pools at single-cell resolution. Here, we present scNT-seq2, a highly sensitive and scalable method for time-resolved single-cell RNA sequencing. By systematically optimizing the second-strand cDNA synthesis (2nd SS) step, we substantially improved read alignment rates, reduced background mutations, and enhanced library complexity compared to the original scNT-seq 1. Benchmarking in 4sU-labeled K562 cells demonstrated that scNT-seq2 accurately quantifies newly synthesized transcripts and preserves the gene level RNA turnover. The enhanced sensitivity enables robust detection of dynamic, cell-cycle state specific genes, such as S-phase regulators. Together, scNT-seq2 provides an efficient and versatile tool for dissecting transcriptional dynamics across diverse biological systems at single-cell resolution.

genomics↗

bioRxiv2021

TRAWLING: a Transcriptome Reference Aware of spLIciNG events.

Alternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events. In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user. Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes. Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis. Availability and implementationOur code is available at https://github.com/Novartis/TRAWLING

bioinformatics↗

bioRxiv2022

Improving the RNA velocity approach using long-read single cell sequencing

The concept of RNA velocity has been recently developed that allowed to look at the otherwise static single-cell RNA sequencing data in a dynamic way, which permitted inferences about cell fates. However, the more precise parameters, such as the number of exons/introns, can also be determined using long-read methods. Comparing the numbers of exons and introns allows including more genes for downstream velocity analysis and resolves the precise cell fate. The recently developed concept of "RNA velocity" concerns with dynamic changes in mRNA expression and complements single-cell RNA sequencing (scRNA-seq) data, which are static snapshots of a certain cell state taken at a given time point1. RNA velocity measures the change in mRNA abundance by differentiating the newly transcribed unspliced pre-mRNAs from mature spliced mRNAs. The rapidly developing long-read sequencing technology lends itself for RNA velocity analysis of scRNA-seq data, which was previously performed primarily using second-generation sequencing.

bioinformatics↗

bioRxiv2019

Systematic comparative analysis of single cell RNA-sequencing methods

A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples - cell lines, peripheral blood mononuclear cells and brain tissue - generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.

genomics↗

bioRxiv2023

Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to vaccination

Single-cell RNA sequencing can to resolve transcriptional features from large numbers of individual immune cells, but techniques capable of resolving the variable regions of B cell receptors (BCR) - defining features that confer antigen specificity to B cells - remain limited, especially from widely-used 3'-barcoded libraries. Here, we report a method that for recovering paired, full-length variable region sequences of the BCRs from 3'-barcoded single-cell whole transcriptome libraries. We first verified this method could produce accurate, full-length BCR sequences. We then applied this method to profile antigen-specific B cell responses elicited against the capsular polysaccharide of Streptococcus pneumoniae serotype 3 (ST3) by glycoconjugate vaccines in infant rhesus macaques. Using our method, we defined features of the BCR associated with specificity for the ST3 antigen and showed that these sequence characteristics are present in multiple vaccinated monkeys, indicating a convergent response to vaccination. These results demonstrate the utility of our method to resolve key features of the B cell repertoire and for profiling antigen-specific responses elicited by vaccination.

immunology↗

bioRxiv2018

NASC-seq monitors RNA synthesis in single cells

Sequencing of newly synthesized RNA can monitor transcriptional dynamics with great sensitivity and high temporal resolution, but is currently restricted to populations of cells. Here, we developed newly synthesized alkylation-dependent single-cell RNA sequencing (NASC-seq), to monitor both newly synthesized and pre-existing RNA in single cells. We validated the method on pre-alkylated exogenous spike-in RNA, and by demonstrating that more newly synthesized RNA was detected for genes with known high mRNA turnover. Importantly, NASC-seq reveals rapidly up- and down-regulated genes during the T-cell activation, and RNA sequenced for induced genes were essentially only newly synthesized. Moreover, the newly synthesized and preexisting transcriptomes after T-cell activation were distinct confirming that we indeed could simultaneously measure gene expression corresponding to two time points in single cells. Altogether, NASC-seq is a powerful tool to investigate transcriptional dynamics and it will enable the precise monitoring of RNA synthesis at flexible time periods during homeostasis, perturbation responses and cellular differentiation.

genomics↗

bioRxiv2020

Nabo - a framework to define leukemia-initiating cells and differentiation in single-cell RNA-sequencing data

Single-cell transcriptomics facilitates innovative approaches to define and identify cell types within tissues and cell populations. An emerging interest in the cancer field is to assess the heterogeneity of transformed cells, including the identification of tumor-initiating cells based on similarities to their normal counterparts. However, such cell mapping is often confounded by the large effects on total gene expression programs introduced by strong perturbations such as an oncogenic event. Here, we present Nabo, a novel computational method that allows mapping of cells from one population to the most similar cells in a reference population, independently of confounding changes to gene expression programs initiated by perturbation. We validated this method on multiple datasets from different sources and platforms and show that Nabo achieves higher rates of accuracy than conventional classification methods. Nabo is available as an integrated toolkit for preprocessing, cell mapping, differential gene expression identification, and visualization of single-cell RNA-Seq data. For exploratory studies, Nabo includes methods to help evaluate the reliability of cell mapping results. We applied Nabo on droplet-based single-cell RNA-Seq data of healthy and oncogene-induced (MLL-ENL) hematopoietic progenitor cells (GMLPs) differentiating in vitro. Despite a substantial cellular heterogeneity resulting from differentiation of GMLPs and the large transcriptional effects induced by the fusion oncogene, Nabo could pinpoint the specific cell stage where differentiation arrest occurs, which included an immunophenotypic definition of the tumor-initiating population. Thus, Nabo allows for relevant comparison between target and control cells, without being confounded by differences in population heterogeneity.

bioinformatics↗

bioRxiv2017

BraCeR: Reconstruction of B-cell receptor sequences and clonality inference from single-cell RNA-sequencing

Reconstruction of antigen receptor sequences from single-cell RNA-sequencing (scRNA-seq) data allows the linking of antigen receptor usage to the full transcriptomic identity of individual B lymphocytes, without having to perform additional targeted repertoire sequencing (Rep-seq). Here we report BraCeR (freely available at https://github.com/teichlab/bracer/), an extension of TraCeR [1], for reconstruction of paired full-length B-cell receptor sequences and inference of clonality from scRNA-seq data (Supplementary Note 1).

bioinformatics↗

bioRxiv2023

Human Pancreatic α-Cell Heterogeneity and Trajectory Inference Analysis Using Integrated Single Cell- and Single Nucleus-RNA Sequencing Platforms

Prior studies have shown that pancreatic -cells can transdifferentiate into {beta}-cells, and that {beta}-cells de-differentiate and are prone to acquire an -cell phenotype in type 2 diabetes (T2D). However, the specific human -cell and {beta}-cell subtypes that are involved in -to-{beta}-cell and {beta}-to--cell transitions are unknown. Here, we have integrated single cell RNA sequencing (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) of isolated human islets and human islet grafts and provide additional insight into -{beta} cell fate switching. Using this approach, we make seven novel observations. 1) There are five different GCG-expressing human -cell subclusters [1, 2, -{beta}-transition 1 (AB-Tr1), -{beta}-transition 2 (AB-Tr2), and -{beta} (AB) cluster] with different transcriptome profiles in human islets from non-diabetic donors. 2) The AB subcluster displays multihormonal gene expression, inferred mostly from snRNA-seq data suggesting identification by pre-mRNA expression. 3) The 1, 2, AB-Tr1, and AB-Tr2 subclusters are enriched in genes specific for -cell function while AB cells are enriched in genes related to pancreatic progenitor and {beta}-cell pathways; 4) Trajectory inference analysis of extracted - and {beta}-cell clusters and RNA velocity/PAGA analysis suggests a bifurcate transition potential for AB towards both - and {beta}-cells. 5) Gene commonality analysis identifies ZNF385D, TRPM3, CASR, MEG3 and HDAC9 as signature for trajectories moving towards {beta}-cells and SMOC1, PLCE1, PAPPA2, ZNF331, ALDH1A1, SLC30A8, BTG2, TM4SF4, NR4A1 and PSCK2 as signature for trajectories moving towards -cells. 6) Remarkably, in contrast to the events in vitro, the AB subcluster is not identified in vivo in human islet grafts and trajectory inference analysis suggests only unidirectional transition from -to-{beta}-cells in vivo. 7) Analysis of scRNA-seq datasets from adult human T2D donor islets reveals a clear unidirectional transition from {beta}-to--cells compatible with dedifferentiation or conversion into -cells. Collectively, these studies show that snRNA-seq and scRNA-seq can be leveraged to identify transitions in the transcriptional status among human islet endocrine cell subpopulations in vitro, in vivo, in non-diabetes and in T2D. They reveal the potential gene signatures for common trajectories involved in interconversion between - and {beta}-cells and highlight the utility and power of studying single nuclear transcriptomes of human islets in vivo. Most importantly, they illustrate the importance of studying human islets in their natural in vivo setting.

cell biology↗