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Draft:Drop-Seq

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Introduction

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Overview of the Drop-seq workflow

Drop-seq is a high-throughput, single-cell RNA sequencing (scRNA-seq) technology used to analyze the mRNA expression of thousands of individual cells by separating them into nanoliter-sized droplets for parallel analysis.

The team behind the development of Drop-seq was led by Dr. Evan Macosko, a Harvard Medical school instructor in psychiatry at Massachusetts General Hospital and a Stanley Neuroscience Fellow in the McCarroll Lab[1]. The limitations of earlier scRNA-Seq methods hindered the fast and scalable characterization of complex tissues, leading to the establishment of Drop-Seq[2].

This method relies on combining a single cell with a uniquely barcoded microparticle (bead) into a single droplet using a microfluidic device. Each cell is lysed within the droplet, releasing its mRNA contents, which binds to an oligo-dT-containing primer sequence on its companion microparticle, capturing polyadenylated mRNA. The primers additionally contain cell barcodes, which are used to identify the cell-of-origin of each transcript, and a unique molecular identifier (UMI), enabling the identification of PCR duplicates. The collection of mRNA-bound microparticles are reverse-transcribed in bulk to form single-cell transcriptomes attached to microparticles (STAMPs). Template switching of reverse-transcriptase is employed to introduce a PCR handle downstream of the synthesized cDNA to enable efficient cDNA amplification. Using a high-throughput sequencer, paired-end reads are generated and aligned to a reference genome to identify the cDNA gene-of-origin and grouped by their cell barcode to determine its cell-of-origin.

History

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The development of sequencing technologies, such as Sanger sequencing and Maxam–Gilbert sequencing, provided the foundation for studying genetic material. Studies of individual transcripts were performed several decades before the emergence of high-throughput sequencing technologies. For instance, in the 1970s, silkmoth mRNA libraries were collected and converted to cDNA using reverse transcriptase[3], and individual transcripts from this library were sequenced using Sanger Sequencing.

In 1976, Walter Fiers and colleagues sequenced the complete transcriptome of bacteriophage MS2[4]. This work laid the foundation for the field of transcriptomics, which is the study of the complete set of RNA transcripts within a given cell, tissue, or organism[5]. Transcriptome sequencing presents challenges due to the characteristics of RNA. The single-stranded structure of RNA causes it to be very unstable and easily degraded by RNases within cells, which requires transcriptome sequencing to be very rigorous[6]. Despite these challenges, progress in transcriptomics has advanced. The first attempt to capture a partial human transcriptome was conducted in 1991 and reported 609 mRNA sequences from the human brain using expressed sequence tags derived from cDNA libraries[7]. While the insights gained from these early studies were valuable, these methods were limited in their abilities to capture the full transcriptome.

In the late 1990s, the development of DNA microarrays enabled the examination of thousands of transcripts at once[8]. Initial studies using microarrays focused on determining differentially expressed genes between normal cells and their corresponding cancerous cells[9]. However, microarrays rely on a priori knowledge of genomic sequences, have limited sensitivity, and are prone to cross-hybridization[10]. The development of RNA-Seq in the mid 2000s enabled comprehensive transcriptome analysis, overcoming some limitations of microarrays, such as their reliance on pre-designed probes, limited sensitivity, and susceptibility of cross-hybridization[11]. This technology can provide insight into functional pathways, regulation of biological processes, alternative splicing, allele-specific expression, and novel transcripts[12].

A caveat of early RNA-seq methods is that they measured the average gene expression levels of all cells in a sample, masking cellular heterogeneity. As a result, RNA-Seq was quickly adapted for single-cell analysis, known as single-cell RNA sequencing (scRNA-Seq), enabling the study of gene expression at the resolution of individual cells[13]. By profiling the transcriptomes of single cells, scRNA-Seq enables the study of cell-type-specific gene expression patterns and rare cell types, playing a crucial role in understanding tumour heterogeneity and evolution[14]. Early scRNA-Seq methods were limited to profiling only hundreds to a few thousand cells per experiment[15][16]. These methods required cells to be separated by flow sorting or microfluidics, followed by the amplification of each cell's transcriptome individually, highlighting the need for fast and scalable approaches to characterize complex tissues under diverse conditions[2].

In 2015, Macosko et al.[2] developed a method to analyze gene expression in thousands of cells in parallel by encapsulating individual cells in nanoliter-sized droplets. Additionally, a molecular barcoding strategy was implemented to trace the cell-of-origin for each transcript, enabling pooled sequencing while maintaining single-cell resolution.

Scientific Principles or Mechanisms

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Drop-seq integrates principles of microfluidics, molecular barcoding and single-cell RNA sequencing to determine gene expression at a single-cell level. Drop-seq’s workflow can be broken down into cell and bead encapsulation, cell lysis and mRNA capture, STAMP-formation, cDNA synthesis and amplification, and bioinformatics analysis [2].

Cell and Bead Encapsulation

The protocol begins by capturing one cell and one uniquely barcoded bead together using a custom microfluidic device. This device consists of two aqueous input channels (one carrying a suspension of cells and the other containing the barcoded beads suspended in lysis buffer) that both flow into an oil channel [2]. Laminar flow prevents the mixing of the aqueous inputs before droplet formation, ensuring that every droplet only contains one cell and one bead [2].

Cell Lysis and mRNA Capture

Immediately after droplet formation, the cells are lysed, releasing mRNAs that then hybridize onto the bead’s surface at the oligo-dT primers [2]. The capture of mRNA within the aqueous bubble ensures that cellular components are not lost [2]. Once hybridization of mRNA onto the bead surface is completed, the beads are broken using a reagent (perfluorooctanol in 30 ml of  6x SSC) that destabilizes the oil phase, releasing the beads into an aqueous solution [2]. This reduces any hybridization effects due to the second-order kinetics of DNA base pairing [2].

Reverse Transcription and cDNA Amplification

After breaking the droplets, the microparticles are washed and resuspended in a reverse transcriptase mix and treated with exonuclease I to remove unextended primers [2]. The mRNAs are then reverse-transcribed in bulk forming single-cell transcriptomes attached to microparticles (STAMPs)[2]. Once the STAMPs are formed, the beads are re-washed, counted, and aliquoted into PCR tubes for cDNA formation and amplification[2]. Template switching adds a second primer at the poly-A tail, allowing the full cDNA to be read. As such, the cDNA molecules will have both the cell barcode and the UMI from the bead [2].

Bioinformatics Analysis

Once sequencing of the cDNA is complete, researchers can determine three things: cell types within the population, gene expression within unique cell types, and gene expression of single cells. From the UMI’s and cellular barcodes, the gene expression of an individual cell can be determined. To determine the cell type, the gene expression is compared to the gene expression of different cell populations to determine cell type probability.

Characteristics or Properties

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The Drop-seq paradigm relies on uniquely barcoded beads and microfluidic droplet encapsulation to capture single cells and their mRNA for sequencing.

Barcoded Beads

One of the main components of Drop-seq is the use of microparticles functionalized with DNA primers that include four main elements for mRNA capture, quantification, and cell identification [2] .

PCR Barcode

Every DNA primer on every bead will have an identical constant region. This is used for downstream PCR and sequencing [2].

Cell Barcodes

A cellular barcode is a sequence found on DNA primers of any one bead, but is unique across beads (every bead has a unique cellular barcode). Synthesis of the cell barcode involves a process known as a “split-and-pool” DNA synthesis strategy [2]. Split-and-pool involves taking all of the microparticle beads and splitting them into four equal groups known as pools [2]. Each pool will then receive one of the four DNA bases [2]. The beads are then mixed together and the whole process is repeated 12 times, ensuring that each bead has a different sequence.

Unique Molecular Identifiers (UMIs)

The unique molecular identifier (UMI) is different on every primer to identify any PCR duplicates, ensuring proper quantification of mRNA [2]. To construct the UMIs, the beads after the initial twelve rounds of split-and-pool DNA synthesis then undergo eight rounds of degenerate oligonucleotide synthesis [2].

Oligo-dT Primers

The end of every oligonucleotide has a sequence of thirty thymines (T30) onto the 3’ end for enzymatic priming [2].

Microfluidic Channels

The microfluidic device is designed to allow co-flows of two aqueous solutions across an oil channel to form over 100,000 nanoliter sized droplets per minute [2].

Applications

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Advancing Breast Cancer Stratification

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Breast cancer patient stratification relies on receptor status and histological grading, but around 20% of patients lack actionable biomarkers, leading to unclear therapeutic intervention[17]. Tumours are composed of multiple subpopulations and display heterogeneity in the estrogen (ESR1), progesterone (PgR), and epidermal growth factor receptor 2 (HER2/ERBB2) biomarkers[18]. Drop-seq was used to measure the gene expression profiles of 35,276 cells from 32 breast cancer cell line subtypes, resulting in the breast cancer single-cell atlas[19]. This atlas can be used to automatically assign the cellular composition of patient tumour biopsies.

Detecting Variability in Response to Drugs

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Tyrosine kinase inhibitors are effective treatments for cancer patients harbouring mutations within receptor tyrosine kinases, yet resistance to these drugs often develops over time[20]. Variability in drug responses has been observed among individual cells within a clonal cell population, leading to varied responses to anticancer therapies[21]. Drop-seq was used to determine cell-to-cell differences in EGFR-mutated NSCLC, revealing that this variability influences treatment responses[22]. It was found that drug-tolerant states arise during treatment, and the distinct combinations of biomarkers identified through drop-seq could serve used as prognostic or therapeutic targets for small-molecule therapies.[22]

Male Meiotic Studies

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The ability of Drop-seq to generate large libraries from mixed cell suspensions has made it the most widely used scRNA-Seq method in male meiotic studies[23]. Spermatogenesis is a complex process in which spermatogonial stem cells undergo terminal differentiation into mature sperm within the testis, driving considerable efforts to understand germ cell differentiation programs [24]. Drop-seq was used to analyze 34,633 cells isolated from mouse testis, which enabled the generation of a detailed cellular and molecular atlas of cell types present in the testis, including 2 previously undescribed adult somatic cell populations[24]. This study further revealed the continuous nature of germ cell development and identified new candidate transcriptional regulators of germ cell differentiation.

References

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