Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Yang, Shiyi; Corbett, Sean; Koga, Yusuke; Wang, Zhe; Johnson, William; Yajima, Masanao; Campbell, Joshua D.

doi:10.1101/704015

Cited by 42 publications

(55 citation statements)

References 8 publications

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“…For experiment 2.01, this results in the following: 1.9% for exponential E. coli, 5.2% for stationary E. coli, 0.92% for S. aureus (left side), and 1.2% for S. aureus (right side). Though higher than the contamination rates observed in the previous species mixing experiment ( Figure S5E,F), these rates are comparable to common eukaryotic scRNA-seq methods 23,24 . Furthermore, we anticipate that contamination could be reduced by additional washing prior to cell lysis (see "Future directions for optimization" in Methods).…”

Section: Figure S5: Quantification Of Intercellular Contamination Usisupporting

confidence: 54%

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Blattman

Jiang

Oikonomou

et al. 2019

Preprint

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Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities. MainRecent developments in high-throughput single-cell RNA sequencing (scRNA-seq) technology have enabled rapid characterization of cellular diversity within complex eukaryotic tissues 1-13 . Despite these advances, comparable tools to study the transcriptomes of individual bacterial cells 14-16 have lagged behind significantly due to numerous technical challenges (Fig. S1). Current massively parallel eukaryotic scRNA-seq methods typically require custom microfluidics to co-encapsulate a single cell with a uniquely barcoded bead in a compartment, often a droplet 5,6,8 or microwell 4,7 . These approaches rely on two key properties of many eukaryotic cells, specifically that they are easily lysed with detergent to release their RNA and that their polyadenylated mRNAs can be effectively captured by beads coated with poly(dT) primers. Adaptation of these approaches for bacteria is thwarted by the presence of thick prokaryotic cell wall 17 , which makes lysis challenging, and the lack of poly-adenylated mRNAs for effective capture.Given these considerations, we identified in situ combinatorial indexing 18 as an alternative basis upon which to develop a method for high-throughput prokaryotic scRNA-seq. Two conceptually similar eukaryotic methods, single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) 11,13 and split-pool ligation-based transcriptome sequencing (SPLiT-seq) 12 , rely on cells themselves as compartments for barcoding, which abrogates the need for cell lysis in droplets or microwells. These methods are also amenable to reverse transcription (RT) with random hexamers instead of poly(dT) primers 12 . With just pipetting steps and no complex instruments, individual t...

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Section: Figure S5: Quantification Of Intercellular Contamination Usisupporting

confidence: 54%

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Blattman

Jiang

Oikonomou

et al. 2019

Preprint

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“…Supervised background correction for the thyrocyte population was performed using DecontX (Yang et al , 2020). As input, normalized data and clustering information from Seurat were used.…”

Section: Methodsmentioning

confidence: 99%

Single‐cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland

et al. 2020

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The thyroid gland regulates growth and metabolism via production of thyroid hormone in follicles composed of thyrocytes. So far, thyrocytes have been assumed to be a homogenous population. To uncover heterogeneity in the thyrocyte population and molecularly characterize the non-thyrocyte cells surrounding the follicle, we developed a single-cell transcriptome atlas of the region containing the zebrafish thyroid gland. The 6249-cell atlas includes profiles of thyrocytes, blood vessels, lymphatic vessels, immune cells, and fibroblasts. Further, the thyrocytes show expression heterogeneity, including bimodal expression of the transcription factor pax2a. To validate thyrocyte heterogeneity, we generated a CRISPR/Cas9-based pax2a knock-in line that monitors pax2a expression in the thyrocytes. A population of pax2a-low mature thyrocytes interspersed in individual follicles can be distinguished. We corroborate heterogeneity within the thyrocyte population using RNA sequencing of pax2a-high and pax2a-low thyrocytes, which demonstrates 20% differential expression in transcriptome between the two subpopulations. Our results identify and validate transcriptional differences within the presumed homogenous thyrocyte population.

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“…This is a common challenge of droplet-based microfluidic library preparation methods [31]. It is crucial to minimize these contaminant signals because they decrease the precision of clustering and the fidelity of downstream analyses like pseudotemporal trajectory analysis [31,32]. Although these can be removed using a number of in silico approaches, the reliability of these tools depends on the various assumptions that may or may not hold true in every biological context [33].…”

Section: Quantification and Statistical Analysismentioning

confidence: 99%

A single-cell atlas of adult Drosophila ovary identifies transcriptional programs and somatic cell lineage regulating oogenesis

et al. 2020

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Oogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modeled extensively using the Drosophila ovary. Although different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic data set for the adult Drosophila ovary and connected tissues. Using this data set, we identified the transcriptional trajectory of the entire follicle-cell population over the course of their development from stem cells to the oogenesis-to-ovulation transition. We further identify expression patterns during essential developmental events that take place in somatic and germline cell types such as differentiation, cell-cycle switching, migration, symmetry breaking, nurse-cell engulfment, eggshell formation, and corpus luteum signaling. Extensive experimental validation of unique expression patterns in both ovarian and nearby, nonovarian cells also led to the identification of many new cell type-and stage-specific markers. The inclusion of several nearby tissue types in this data set also led to our identification of functional convergence in expression between distantly related cell types such as the immune-related genes that were similarly expressed in immune cells (hemocytes) and ovarian somatic cells (stretched cells) during their brief phagocytic role in nurse-cell engulfment. Taken together, these findings provide new insight into the temporal regulation of genes in a cell-type specific manner during oogenesis and begin to reveal the relatedness in expression between cell and tissues types.

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Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Cited by 42 publications

References 8 publications

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Single‐cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland

A single-cell atlas of adult Drosophila ovary identifies transcriptional programs and somatic cell lineage regulating oogenesis

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