A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

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Single cell transcriptome atlas of mouse mammary epithelial cells across development

Breast Cancer Research ◽

10.1186/s13058-021-01445-4 ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Bhupinder Pal ◽

Yunshun Chen ◽

Michael J. G. Milevskiy ◽

François Vaillant ◽

Lexie Prokopuk ◽

...

Keyword(s):

Epithelial Cells ◽

Single Cell ◽

Developmental Stages ◽

Mammary Epithelial Cells ◽

Expression Profiles ◽

Single Cells ◽

Chromatin Accessibility ◽

Mammary Epithelial ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

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Single cell transcriptome analysis reveals markers of naïve and lineage-primed hematopoietic progenitors derived from human pluripotent stem cells

10.1101/602565 ◽

2019 ◽

Author(s):

Antonella Fidanza ◽

Nicola Romanò ◽

Prakash Ramachandran ◽

Sara Tamagno ◽

Martha Lopez-Yrigoyen ◽

...

Keyword(s):

Stem Cells ◽

Pluripotent Stem Cells ◽

Single Cells ◽

Human Pluripotent Stem Cells ◽

In Vitro Differentiation ◽

Hematopoietic Progenitors ◽

Lineage Priming ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractDuring embryogenesis the hematopoietic system develops through distinct waves that generate progenitors with increasing lineage potential, ultimately producing haematopoietic stem cells (HSCs). In vitro differentiation of human pluripotent stem cells (hPSCs) follows the early steps of haematopoietic development but the production of HSCs has proven more challenging. To study the dynamics and heterogeneity of hematopoietic progenitor cells generated in vitro from hPSCs, we performed RNA sequencing of over 10000 CD235a-CD43+single cells. We identified the transcriptome of naïve progenitors and those primed toward erythroid, megakaryocyte and leukocyte lineages, and revealed their markers by clustering, trajectory analyses and functional assays. CD44 marks naïve clonogenic progenitors that express the transcription factor, LMO4 and can be expanded upon BMP4 stimulation. Naïve progenitors give rise to primed CD326+erythroid, ICAM2+CD9+megakaryocyte, and monocyte, neutrophil and eosinophil progenitors. We have generated an online dataset of human hematopoietic progenitors and their transcriptional remodelling upon lineage priming.

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Representation learning of RNA velocity reveals robust cell transitions

10.1101/2021.03.19.436127 ◽

2021 ◽

Author(s):

Chen Qiao ◽

Yuanhua Huang

Keyword(s):

Cellular Differentiation ◽

Representation Learning ◽

Biological Processes ◽

Dimensional Representation ◽

Promising Technique ◽

Technical Noise ◽

Wide Range ◽

Cell Transcriptome ◽

Low Dimensional ◽

Single Cell Transcriptome

RNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.

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BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

10.1101/392662 ◽

2018 ◽

Author(s):

Zhe Sun ◽

Li Chen ◽

Hongyi Xin ◽

Qianhui Huang ◽

Anthony R Cillo ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

R Package ◽

Clustering Methods ◽

Model Framework ◽

Bayesian Hierarchical ◽

Bayesian Mixture ◽

Population Scale ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

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Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

10.1101/2021.09.15.460240 ◽

2021 ◽

Author(s):

Fredrik Salmen ◽

Joachim De Jonghe ◽

Tomasz S. Kaminski ◽

Anna Alemany ◽

Guillermo Parada ◽

...

Keyword(s):

Alternative Splicing ◽

Single Cell ◽

Single Cells ◽

Droplet Microfluidics ◽

Mammalian Development ◽

Mouse Development ◽

Protein Coding ◽

Cell Transcriptome ◽

Single Cell Transcriptome

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA- seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate- based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

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Evolution of Developmental Programs for the Midline Structures in Chordates: Insights From Gene Regulation in the Floor Plate and Hypochord Homologues of Ciona Embryos

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.704367 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kouhei Oonuma ◽

Maho Yamamoto ◽

Naho Moritsugu ◽

Nanako Okawa ◽

Megumi Mukai ◽

...

Keyword(s):

Reporter Gene ◽

Single Cells ◽

Regulatory Region ◽

Ectopic Expression ◽

Floor Plate ◽

Dorsal Midline ◽

Extracellular Signaling ◽

The Central Nervous System ◽

Cell Transcriptome ◽

Single Cell Transcriptome

In vertebrate embryos, dorsal midline tissues, including the notochord, the prechordal plate, and the floor plate, play important roles in patterning of the central nervous system, somites, and endodermal tissues by producing extracellular signaling molecules, such as Sonic hedgehog (Shh). In Ciona, hedgehog.b, one of the two hedgehog genes, is expressed in the floor plate of the embryonic neural tube, while none of the hedgehog genes are expressed in the notochord. We have identified a cis-regulatory region of hedgehog.b that was sufficient to drive a reporter gene expression in the floor plate. The hedgehog.b cis-regulatory region also drove ectopic expression of the reporter gene in the endodermal strand, suggesting that the floor plate and the endodermal strand share a part of their gene regulatory programs. The endodermal strand occupies the same topographic position of the embryo as does the vertebrate hypochord, which consists of a row of single cells lined up immediately ventral to the notochord. The hypochord shares expression of several genes with the floor plate, including Shh and FoxA, and play a role in dorsal aorta development. Whole-embryo single-cell transcriptome analysis identified a number of genes specifically expressed in both the floor plate and the endodermal strand in Ciona tailbud embryos. A Ciona FoxA ortholog FoxA.a is shown to be a candidate transcriptional activator for the midline gene battery. The present findings suggest an ancient evolutionary origin of a common developmental program for the midline structures in Olfactores.

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Pooled CRISPR screening with single-cell transcriptome read-out

10.1101/083774 ◽

2016 ◽

Cited By ~ 3

Author(s):

Paul Datlinger ◽

Christian Schmidl ◽

André F Rendeiro ◽

Peter Traxler ◽

Johanna Klughammer ◽

...

Keyword(s):

Single Cell ◽

Selectable Marker ◽

Single Cells ◽

Transcriptome Profiling ◽

Genetic Screens ◽

Biological Mechanisms ◽

Guide Rna ◽

New Gene ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractCRISPR-based genetic screens have revolutionized the search for new gene functions and biological mechanisms. However, widely used pooled screens are limited to simple read-outs of cell proliferation or the production of a selectable marker protein. Arrayed screens allow for more complex molecular read-outs such as transcriptome profiling, but they provide much lower throughput. Here we demonstrate CRISPR genome editing together with single-cell RNA sequencing as a new screening paradigm that combines key advantages of pooled and arrayed screens. This approach allowed us to link guide-RNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow.

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Single Cell Transcriptome Conservation in Cryopreserved Cells and Tissues

10.1101/067884 ◽

2016 ◽

Author(s):

Amy Guillaumet-Adkins ◽

Gustavo Rodríguez-Esteban ◽

Elisabetta Mereu ◽

Alberto Villanueva ◽

August Vidal ◽

...

Keyword(s):

Single Cell ◽

Future Study ◽

Paradigm Shift ◽

Single Cells ◽

Sample Preservation ◽

Preservation Method ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Study Designs ◽

Processing Steps

AbstractA variety of single cell RNA preparation procedures have been described. So far these protocols require fresh starting material, hindering complex study designs. We describe a sample preservation method that maintains transcripts in viable single cells and so allows to disconnect time and place of sampling from subsequent processing steps. To demonstrate the potential, we sequenced single cell transcriptomes from >1,000 fresh and conserved cells. Our results confirmed that the conservation process did not alter transcriptional profiles. This substantially broadens the scope of applications in single cell transcriptomics and could lead to a paradigm shift in future study designs.

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Targeting individual cells by barcode in pooled sequence libraries

10.1101/178681 ◽

2017 ◽

Cited By ~ 1

Author(s):

Navpreet Ranu ◽

Alexandra-Chloé Villani ◽

Nir Hacohen ◽

Paul C. Blainey

Keyword(s):

Single Cell ◽

Mononuclear Cells ◽

Single Cells ◽

Dna Barcodes ◽

Rna Seq ◽

Enrichment Method ◽

Peripheral Blood Mononuclear ◽

Efficient Sampling ◽

Cell Transcriptome ◽

Single Cell Transcriptome

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

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Single-cell transcriptome profiling of the Ciona larval brain

10.1101/319327 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sarthak Sharma ◽

Wei Wang ◽

Alberto Stolfi

Keyword(s):

Single Cell ◽

Single Cells ◽

Expression Patterns ◽

Transcriptome Profiling ◽

Specific Gene ◽

Larval Brain ◽

Rnaseq Data ◽

Cell Type Specific ◽

Cell Transcriptome ◽

Single Cell Transcriptome

AbstractThe tadpole-type larva of Ciona has emerged as an intriguing model system for the study of neurodevelopment. The Ciona intestinalis connectome has been recently mapped, revealing the smallest central nervous system (CNS) known in any chordate, with only 177 neurons. This minimal CNS is highly reminiscent of larger CNS of vertebrates, sharing many conserved developmental processes, anatomical compartments, neuron subtypes, and even specific neural circuits. Thus, the Ciona tadpole offers a unique opportunity to understand the development and wiring of a chordate CNS at single-cell resolution. Here we report the use of single-cell RNAseq to profile the transcriptomes of single cells isolated by fluorescence-activated cell sorting (FACS) from the whole brain of Ciona robusta (formerly intestinalis Type A) larvae. We have also compared these profiles to bulk RNAseq data from specific subsets of brain cells isolated by FACS using cell type-specific reporter plasmid expression. Taken together, these datasets have begun to reveal the compartment- and cell-specific gene expression patterns that define the organization of the Ciona larval brain.

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